Views of New Testament Textual Space


Table of Contents

Abstract
Introduction
Computing Environment
Data Sets
Analysis Methods
Ranked Distances
Classical Multidimensional Scaling (CMDS)
Divisive Clustering (DC)
Neighbour Joining (NJ)
Partitioning Around Medoids (PAM)
Subtraction
Analysis Results
All Data Sets (CMDS, DC, and NJ)
Selected Data Sets (PAM)
Gospels
Acts and General Letters
Paul's Letters
Revelation
Patristic Witnesses (Ranked Distances)
Discussion
Selected Data Sets
Gospels
Acts and General Letters
Paul's Letters
Revelation
Patristic Witnesses
Ramblings (draft, do not quote)
UBS2
Matthew
Luke
John
Acts
UBS4
Mark
2 Corinthians
Galatians
Hebrews
1 Peter
1 John
The Apocalypse
INTF General Letters
James
1 Peter
2 Peter
1 John
2 John
3 John
Jude
INTF Parallel Pericopes
INTF Parallel, Luke
Brooks
Cosaert
Cunningham
Donker
EFH (Ehrman, Fee, and Holmes)
Ehrman
Mullen
Osburn
Racine
Wasserman
What Difference Does It Make?
Conclusions (draft, do not quote)
Acknowlegments
A. Supplementary Information
Bibliography

Note

This is a work in progress.

The Greek New Testament was copied by hand for almost fifteen centuries until the advent of mechanized printing provided an alternative means of propagation. Translations into other languages were produced as well. Some of these — such as the Latin, Coptic, and Syriac versions — appeared early and thus preserve ancient states of the text. Patristic citations form another class of evidence that allows varieties of the text to be associated with particular localities and epochs. Analysis of textual variation allows relationships between these three classes of witnesses — manuscripts, versions, and patristic citations — to be explored. This article applies various kinds of analysis to textual variation data collected from a variety of sources. The analysis results offer complementary views of the textual space occupied by these venerable witnesses to the New Testament text.

As with every widely read work from antiquity, the New Testament exhibits textual variation introduced by scribes and correctors. Sites where textual variation occurs are identified by comparing extant witnesses of the text.[1] Differences may be classified as orthographic or substantive. Orthographic variations are often ignored as they only affect the surface form of a text and not its meaning. Substantive variations do affect meaning: they are often called readings or variants The list of witnesses that support a particular reading of a particular variation site is known as the reading's attestation. A list of all readings at a variation site along with the attestation of each reading is called a variation unit. Critical editions often present variation units in an apparatus. The present study is based on analysis of data sets extracted from critical editions, monographs, journal articles, dissertations, or online databases.

There is an ongoing effort to establish the initial text which stands behind the range of texts found among surviving witnesses of the New Testament.[2] The most important witnesses for establishing the initial text fall into these categories:

  • Greek manuscripts

  • ancient versions

  • patristic citations.

Greek manuscripts are the primary witnesses to the text of the New Testament. Ancient versions are early translations of the Greek text into languages such as Latin, Coptic, Syriac, and Armenian. It is often possible to establish which Greek reading a version supports by translating its text at a variation site back into Greek. Patristic citations are quotations of the scripture by Church Fathers. Which reading was in a Church Father's copy of the text at a particular variation site can often be discerned if that part of the text is covered by one of his quotations.

A large proportion of the textual evidence disappeared long ago. Even a comprehensive data set that includes all readings of all extant witnesses is still a mere sample of what once existed. In general, the older the copy, the more likely it is to have been lost. This loss of data presents a fundamental problem: if extant texts do not represent the oldest copies then the survivors will give a skewed impression of the initial text. Happily, there is a way forward: if like texts are grouped and more or less accurate representations of the archetypal texts that gave rise to the groups can be reconstructed then these archetypes have a claim to being more representative of the ancient text. In addition, the extant copies provide data for estimating how accurately the copyists practiced their art. Armed with a knowledge of the number of generations of copies and typical rates of substituting one reading for another per generation, it is possible to say how much the original text is likely to differ from the initial text recoverable from extant copies. While raw data that could be used for the purpose is presented below, I do not propose to reconstruct hypothetical archetypes or estimate rates of change here. Instead, this article will focus on presentation and analysis of available data to explore grouping among the extant texts.[3]

Even though much is lost, a stupendous amount of evidence remains. There are many thousands of manuscripts in Greek, Latin, Syriac, Armenian, and other languages. Patristic citations are also very numerous. Given such a great cloud of witnesses, it can be difficult to see where each one stands in relation to the others. Fortunately, various methods of statistical analysis can be applied to data sets which relate to textual variation in order to explore relationships among the witnesses.

Analysis might begin from a number of starting points. One suitable place to begin is a data set derived from a critical apparatus which gives attestations (i.e. lists of witnesses) in support of readings found at variation sites. In nearly all cases, practical considerations restrict an apparatus to presenting a sample of extant texts. Results obtained by analysis of these data sets are therefore provisional because it is always possible that including further data would produce different results. However, it is reasonable to expect that analysis results will approximate those that would be obtained if a more comprehensive data set were analysed provided that the sample is sufficiently large and has been selected without systematic bias.

The information contained in an apparatus must first be encoded as illustrated by reference to this entry from the fourth edition of the United Bible Societies' Greek New Testament (UBS4):


The data sets presented in this article use a number of encoding conventions. Exotic characters and superscripts can cause problems when plotting analysis results so witness identifiers (i.e. sigla) are Romanized and superscripts are replaced by hyphenated sequences of characters. Apart from these changes, the method of identifying witnesses used by the source of a data set is usually retained. Be warned, dear reader: this approach is liable to cause confusion when two sources use different identifiers for the same witness. For example, Codex Sinaiticus may be identified as Aleph or 01. Also, the critically established text used in the INTF's Editio Critica Maior may be referred to as A (for Ausgangstext), making it easy to confuse with the A often used to represent Codex Alexandrinus.

When it comes to encoding apparatus entries, the textual states found among the witnesses can be represented by numerals, letters, or other symbols. In the present example, the first reading is encoded as 1, the second as 2, and so on. The state of a witness is classified as undefined and encoded as NA (for not available) when it is not clear which reading the witness supports. For manuscripts this may be due to physical damage or because the manuscript does not include the section of text being examined; for versions, it may not be clear which state of the Greek text is supported by a back-translation of the version; for patristic citations, the reading of a Church Father's text may be unclear if the quotations are not exact (e.g. adaptations, allusions, or quotations from memory) or if different witnesses of the Church Father's text have different readings. In the present example, a number of versions (Latin, Syriac, Coptic) and patristic citations (e.g. those of Irenaeus, Ambrose, Chromatius, Jerome, and Augustine) are treated as undefined because it is not clear which readings they support at this variation site.[4]


Encoded readings are entered into a data matrix which has a row for every witness and a column for every variation site. The appropriate code is entered at the cell corresponding to a particular witness and variation site, namely that cell located at the intersection of the witness row and variation site column. Manuscript correctors are treated as separate witnesses, as are supplements.


The next step is to construct a distance matrix which tabulates the simple matching distance between each pair of witnesses sufficiently represented in the data set. The simple matching distance between two witnesses is the proportion of disagreements between them at those variation sites where the textual states of both are defined. Being a ratio of two pure numbers, this quantity is dimensionless (i.e. has no unit). It varies from a value of zero for complete agreement to a value of one for no agreement.[5] A witness only qualifies for inclusion in a distance matrix if all distances for that witness are calculated from at least a minimum number of variation sites. This constraint is intended to reduce sampling error to a tolerable level. It is enforced by a vetting algorithm that progressively drops witnesses with the least numbers of defined variation sites until all distances in the distance matrix are guaranteed to have been calculated from a minimum acceptable number of sites. The minimum acceptable number for the distance matrices of this study is nearly always set at fifteen.[6]


Various analytical methods can applied to a data set derived from a critical apparatus to explore relationships between witnesses. All of the results presented in this article are obtained using a statistical computing language called R. The analysis is performed by means of R scripts written by the author which are available here. The R program and additional packages (e.g. cluster, rgl, ape) required to run the scripts can be installed using instructions provided at the R web site.

Readers are encouraged to use the scripts. There are various ways to run a script once the R environment is installed. For users who prefer a command line interface, typing R into a terminal window provides an R prompt. (It helps to change to the directory which holds the scripts before launching R.) A command can then be entered in order to run a script. As an example, the command source("dist.r") typed at the R prompt causes the dist.r script to construct a distance matrix from the specified data matrix. Parameters such as paths to input and output files are specified in the scripts, which users are free to edit.

The data sets analysed in this article derive from various sources. Each source is assigned an identifier based on the author or party who produced it. A source is often used to produce data sets for a number of New Testament sections such as individual gospels and letters. Each analysis result is keyed to the relevant section and source identifier so that its underlying data set can be identified.

The data sets generally retain the symbols used by their associated sources to represent New Testament witnesses. Some represent manuscripts by Gregory-Aland numbers (e.g. 01, 02, 03, 044) while others use letters or latinized forms (e.g. Aleph, A, B, Psi). These symbols carry through to the analysis results. In INTF data, ECM or A (for Ausgangstext or initial text) represents the text of the Editio Critica Maior. The A for Ausgangstext in INTF data sets should not be confused with the A for Codex Alexandrinus in other data sets. Abbreviations UBS, WH, and TR stand for the texts of the United Bible Societies' Greek New Testament, Westcott and Hort's New Testament in the Original Greek, and the Textus Receptus, respectively. Maj, Byz, and Lect stand for majority, Byzantine, and lectionary texts, respectively. The relevant printed editions should be consulted for explanations of what these group symbols represent.

A source may be in the form of apparatus entries, tables of percentage agreement, or lists of pairwise proportional agreement. If the source is an apparatus then it is used to construct one data matrix per desired section. Each data matrix includes those witnesses and variation sites covered by the apparatus, using symbols such as numerals or letters to encode reported textual states (i.e. readings). A distance matrix is then constructed from the data matrix. If the source only reports percentage or proportional agreement between witnesses then a distance matrix is constructed directly from the agreement data and no data matrix is produced. Distances are usually specified to three decimal places regardless of whether this level of precision is warranted.

Analysis cannot proceed if a distance matrix has missing entries. This problem can be avoided by manually producing multiple distance matrices from the same source data, each omitting a particular witness whose inclusion would create an empty cell. This is done for a number of the distance matrices presented below, including Brooks' table for John (where there is a missing cell for C and Old Latin j) and Fee's table for John 1-8 (which lacks cells where the first hand and corrector intersect for P66 and Aleph).

Distance matrices are normally obtained by applying the default vetting algorithm, which drops the least defined witness of each pair used to calculate a distance until all distances are calculated from the minimum acceptable number of variation sites where both are defined, which is normally fifteen. In some cases, an alternative approach is used which forces a particular witness to be retained provided it has enough defined variation sites at the outset. Examples include UBS2 distance matrices for Mark, John, and Acts where P45, Ephraemi Rescriptus (C), and Sinaiticus (Aleph) have been retained due to their importance.

It is helpful to know what analysis results look like when there is no clustering among the objects being analysed. (Generic terms such as object, observation, case, or item may be used for the things being compared when they are not necessarily New Testament witnesses.) We have a natural facility for recognising group structure but are also prone to mistake a purely random distribution of items for a cluster. One way to avoid this kind of error is to be familiar with analysis results produced from a data set that has no group structure. With this purpose in mind, a control data set may be generated which is analogous to its model data set in various respects (e.g. number of objects, number of variables, mean distance between objects) but has no actual clustering among its objects.

A control data set is generated by performing c trials to randomly select one of two possible states (1 and 2) then repeating this r times to produce a data matrix with r rows of objects and c columns of variables. The generator aims to produce objects which have a mean distance of d between them. Values for r, c, and d are derived from the model: r is the number of objects in the model distance matrix; c is the rounded mean number of variables in the objects from which the model distance matrix was calculated; and d is the mean of distances in the model distance matrix. The control data matrix is then used to calculate a control distance matrix which has the same number of objects as the model and approximately the same mean distance between objects.[7]

The binomial distribution predicts the range of distances expected to occur between pairs of objects generated in this way. A 95% confidence interval is the range of distances expected to occur for 95% of randomly generated cases. Only 5% of distances between two randomly generated objects fall outside the upper and lower limits defined by this interval. A distance outside this range, either less or more, is statistically significant in the sense that it is unlikely to happen by chance (though there is a 5% chance it will). A distance outside the normal range defined by the 95% confidence interval indicates an adjacent or opposite relationship between two objects: adjacent if the distance is less than normal and opposite if greater.[8]

While distances outside the normal range are unlikely to occur by chance, a distance inside that range does not necessarily imply lack of relationship between two objects: a relationship between the two may exist but it is not possible to say so with confidence. The relative size of the normal range contracts as the number of places compared increases so a distance which is not statistically significant in one data set may be statistically significant in another which includes more variation sites.

The following table presents the data sets and their sources. Links in the table provide access to data and distance matrices which are formatted as comma-separated vector (CSV) files so that they can be downloaded and imported into a spreadsheet program. A distance matrix is always provided but a data matrix is only included if one has been constructed. If there is no data matrix then NA for not available is entered in the relevant column.

Table 2. Data sets and their sources

Source Description Section Data matrix Distance matrix
Brooks Tables of percentage agreement from James Brooks' New Testament Text of Gregory of Nyssa covering: Matthew (table 1, 58-9); Luke (table 7, 90-1); John (table 13, 138-9); and Paul's Letters (table 18, 254-5). These were transcribed by Richard Mallett. Matthew NA
Luke NA
John (C) NA
John (it-j) NA
Paul's Letters NA
CB Data matrices for each Gospel compiled by Richard Mallett using Comfort's New Testament Text and Translation Commentary and Comfort and Barrett's Text of the Earliest New Testament Greek Manuscripts. Matthew
Mark
Luke
John
Cosaert Data matrices for each Gospel compiled from apparatus entries in Carl P. Cosaert's Text of the Gospels in Clement of Alexandria. Matthew
Mark
Luke
John
Cunningham Tables of percentage agreement for the Gospel of John and Paul's Letters from Arthur Cunningham's New Testament Text of St. Cyril of Alexandria, 421-2 and 753. Associated tables of counts are on pages 423-4 and 754. John NA
Paul's Letters NA
Donker Data matrices for Acts, the General Letters, and Paul's Letters from Gerald Donker's Text of the Apostolos in Athanasius of Alexandria. Gerald Donker and the SBL have made this data available through an archive located at sbl-site.org/assets/pdfs/pubs/Donker/Athanasius.zip. May their respective tribes increase! Acts (all)
Acts 1-12
Acts 13-28
General Letters
Paul's Letters
Romans
1 Corinthians
2 Cor. - Titus
Hebrews
EFH Data used by Jared Anderson for his ThM thesis, Analysis of the Fourth Gospel in the Writings of Origen. The data was originally collected by Bart D. Ehrman, Gordon D. Fee, and Michael W. Holmes for their Text of the Fourth Gospel in the Writings of Origen. (Bruce Morrill did the statistical analysis presented in that volume.) A revised version of Anderson's thesis will be published in SBL's New Testament in the Greek Fathers series. John
Ehrman Table of percentage agreement for the Gospel of Matthew from Bart Ehrman's Didymus the Blind and the Text of the Gospels. This was transcribed by Richard Mallett. Matthew NA
Fee Tables of percentage agreement from three articles by Gordon Fee: (1) a table covering Luke 10 from The Myth of Early Textual Recension in Alexandria; (2) tables covering John 1-8, John 4, and John 9 from Codex Sinaiticus in the Gospel of John; (3) another table covering John 4 but including patristic data from The Text of John in Origen and Cyril of Alexandria. Two distance matrices are produced for each table of percentage agreement with a blank entry for agreement between the first hand and corrector of a manuscript. Luke 10 NA
John 1-8 NA
John 1-8 (corr.) NA
John 4 NA
John 4 (corr.) NA
John 4 (pat.) NA
John 4 (pat., corr.) NA
John 9 NA
John 9 (corr.) NA
Hurtado Tables of percentage agreement from Larry Hurtado's Text-Critical Methodology and the Pre-Caesarean Text. There is one table for each of the first fourteen chapters of the Gospel of Mark, one for Mark 15.1-16.8, and another for places where P45 is legible. Data from an augmented version of Hurtado's P45 table is presented below in the Mullen source entry. Mark 1 NA
Mark 2 NA
Mark 3 NA
Mark 4 NA
Mark 5 NA
Mark 6 NA
Mark 7 NA
Mark 8 NA
Mark 9 NA
Mark 10 NA
Mark 11 NA
Mark 12 NA
Mark 13 NA
Mark 14 NA
Mark 15.1-16.8 NA
Mark (P45) NA
INTF-General Distance matrices derived from information in a database related to the INTF's Novum Testamentum Graecum: Editio Critica Maior: Catholic Letters volumes. The INTF kindly granted access to this data. James NA
1 Peter NA
2 Peter NA
1 John NA
2 John NA
3 John NA
Jude NA
INTF-Parallel Distance matrices made from tables located at http://intf.uni-muenster.de/PPApparatus/. These present data related to Strutwolf and Wachtel (eds.), Novum Testamentum Graecum: Editio Critica Maior: Parallel Pericopes. The INTF has generously provided open access to this data. Matthew
Mark
Luke
John
Mullen Data extracted from Roderic Mullen's The New Testament Text of Cyril of Jerusalem. Two data sets have been prepared for the Gospel of Mark: one is a data matrix based on citations isolated by Mullen (112-7); the other is a distance matrix corresponding to a table of percentage agreement which relates to the parts of Mark's Gospel covered by P45 (41). Mullen based the latter on data compiled by Larry Hurtado then added other texts such as Family 1, 28, 157, and 700 (40, n. 81). Mark
Mark (P45) NA
Osburn Tables of percentage agreement for Acts and Paul's Letters from Carroll Osburn's Text of the Apostolos in Epiphanius of Salamis. Richard Mallett transcribed these tables. Acts NA
Paul's Letters NA
Racine Table of percentage agreement for Matthew's Gospel from Jean-François Racine's Text of Matthew in the Writings of Basil of Caesarea. This was transcribed by Richard Mallett. Matthew NA
Richards Table of percentage agreement from W. L. Richards' Classification of the Greek Manuscripts of the Johannine Epistles (72, 76-84). 1 John NA
UBS2 Tables of percentage agreement compiled from the apparatus of the second edition of the UBS Greek New Testament by Maurice A. Robinson. The tables were originally presented in Robinson's Determination of Textual Relationships and Textual Interrelationships. They were transcribed by Claire Hilliard and Kay Smith. Matthew NA
Mark (C) NA
Luke NA
John (Aleph) NA
Acts (Aleph) NA
Acts (P45) NA
UBS4 Data matrices constructed from the apparatus of the fourth edition of the UBS Greek New Testament. (The UBS4 apparatus includes minuscule 2427, which is now regarded as a forgery. The data for this manuscript has been retained for the sake of interest; dropping it would have little effect on analysis results.) Richard Mallett constructed the matrices for Mark, 2 Corinthians, and Revelation. A substantial part of the matrix for Matthew was encoded by Mark Spitsbergen. (Only the first fourteen chapters of Matthew are presently covered.) In some cases, the evidence for a number of similar witnesses is consolidated to produce a group reading. For example, the majority reading of vg-cl, vg-st, and vg-ww is counted as the reading of the Vulgate (vg) in 1 John. Matrices for Mark derived as controls or by excluding readings found in representatives of five textual groups (B = Vaticanus, Byz = Byzantine, it-ff-2 = Old Latin ff2, f-1 = Family 1, vg = Jerome's Vulgate) are included as well. PAM analysis (see below) was used to select the representatives. Variants were excluded by script mask.R, which for each witness drops (by substituting NA) those readings that match the representative text. Matthew 1-14
Mark
Mark (control)
Mark (non-B)
Mark (non-Byz)
Mark (non-f-1)
Mark (non-it-ff-2)
Mark (non-vg)
1 Peter
1 John
2 Corinthians
Hebrews
Revelation
Wasserman Tables of proportional agreement from Tommy Wasserman's Patmos Family of New Testament MSS covering Matt 19.13-26, Mark 11.15-26, Luke 13.34-14.11, John 6.60-7.1, and the Pericope Adulterae (usually John 7.53-8.11). The underlying collations used a reconstructed text to represent Family Π in Matt 19.13-26 and the Pericope Adulterae, which text is labelled f-Pi in the analysis results. Matthew 19.13-26 NA
Mark 11.15-26 NA
Luke 13.34-14.11 NA
John 6.60-7.1 NA
PA NA

This study presents results obtained by applying the following analysis methods to the data sets:

These analysis modes will now be introduced by reference to two data sets:

  1. a model derived from the UBS4 apparatus for the Gospel of Mark

  2. a control comprised of randomly generated objects which are by definition unrelated.

Clusters may be isolated by inspecting a CMDS or NJ plot, cutting a DC dendrogram, or producing a partition using PAM analysis. Similar objects tend to be similar distances from a reference object, near each other in a CMDS plot, in the same branch of DC and NJ plots, and in the same group of a PAM partition. The more eccentric an object when compared to others in the data set, the more isolated it will appear in analysis results. If an object is mixed, being comprised of a mixture of states characteristic of differing groups, then a CMDS result will locate it between the relevant groups, proportionally closer to those whose characteristics it most often contains. In DC, NJ, and PAM analysis, a slight change in the distance matrix can cause a mixed object to leap from one branch, cluster, or group to another.

The respective analysis results are often but not always consistent. If all of the analysis results point to the same conclusion with respect to implied clustering then that can be taken as a firm result; if they differ then each result needs to be handled with due caution. The distance matrix remains the final arbiter when the affiliation of an object is not clearly indicated by concurrence of analysis results. When the classification of an object is uncertain, further information may produce a more definite result. However, if an object has a mixed nature then it may remain difficult to classify as anything but a mixture. A mixed object will tend to be isolated unless other objects happen to have similar mixtures of states.

One aim of New Testament textual research is to recover the initial text, namely the common ancestor of extant New Testament texts. Some aspects of the results produced by the analysis modes used in this study can be interpreted in terms of temporal development. In particular, there may be points of contact between the the family tree of New Testament texts and the tree-like structures produced by divisive clustering and neighbour joining. However, these tree-like analysis results do not provide unequivocal guidance on the location of the initial text. Any node (i.e. junction) or leaf (i.e. terminal) of a DC or NJ tree could be closest to the initial text. If one were to make a string model of such a tree, with knots at every node tying together string segments of the appropriate lengths, the model could be picked up at any node or leaf. The point being held would become a new tree root so there would be as many possible trees as the number of nodes and leaves. The trick is to decide where the root of the tree is located, a topic which will occupy the field of New Testament textual research for some time to come. The Coherence-based Genealogical Method (CBGM) developed by the INTF can be used to investigate whether the witnesses in one branch are closer to the initial text than those in another.[9] Phylogenetic techniques such as described in Spencer, Wachtel and Howe's Greek Vorlage of the Syra Harclensis can also be used to investigate the priority of texts. Yet another possibility is to see where texts reconstructed from early patristic citations are located in trees produced by DC and NJ analysis.

Ranking involves selecting a reference object then extracting its row of the distance matrix. Entries in that row are then ordered by increasing distance from the reference. As an example, the following ranks witnesses in the UBS4 data set for the Gospel of Mark by distance from minuscule 205, which is a member of Family 1. The reference witness (i.e. 205) is a distance of zero from itself and would stand at the head of the list if included.[10]


Statistical analysis shows what range of distances is expected to occur between artificial objects comprised of randomly selected states. Distances in this normal range (i.e. those for 1243, slav, ..., Psi) are marked by asterices to show they are not statistically significant. Some texts (i.e. f-1, Lect, ..., 597) have an adjacent relationship to minuscule 205 while others (i.e. Delta, cop-bo, ..., D) are opposite.

A ranked list of distances from one member of the control data set shows what to expect for unrelated objects. The 95% confidence interval calculated using the binomial distribution with parameters derived from the model data set has lower and upper bounds of 0.374 and 0.553, respectively. (An interval of this kind can be compactly written as [0.374, 0.553].) As can be seen, distances in the control data set tend to fall within these bounds.[11]


A list of ranked distances can be produced for every object in a data set. While clustering among members of the data set might be discerned from lists of this kind, the other analysis modes are better suited to discovering inherent group structure.

Classical multidimensional scaling finds the set of object coordinates which best reproduces the actual distances between objects in the distance matrix. A plot of these coordinates shows how the objects are disposed with respect to one another when all distances are considered. This study refers to such a plot as a map and uses the term textual space for the space obtained when the objects are textual witnesses.

Achieving a perfect spatial representation of a distance matrix may require any number of dimensions up to one less than the number of objects. This presents a problem when a large number of objects is being examined because our spatial perception is three-dimensional. Fortunately, three dimensions is often sufficient to achieve a reasonably good approximation to the actual situation. CMDS analysis produces a coefficient called the proportion of variance which indicates how much of the information contained in a distance matrix is explained by the associated map. This coefficient ranges from a value of zero to one, with a value of one indicating that the map is a perfect representation of the entire set of actual distances.

The CMDS map obtained from the UBS4 data set for Mark's Gospel shows that the textual space formed by New Testament witnesses has structure. The associated proportion of variance figure is 0.51, meaning that about half of the entire distance information is captured in the plot.[12]


The galactic imagery Eldon J. Epp uses to describe text-types seems apt for the clusters evident in this analysis result:

A term such as group, cluster, or nucleus might be used to describe a local maximum in the density of objects within a CMDS map. A line which joins two items might be called a trajectory, and a region between groups where there is a higher than usual concentration of witnesses might be called a stream or corridor.[14]

CMDS analysis of the control distance matrix produces the following result:


Any appearance of clustering in the control map is illusory: its objects are by definition unrelated, having been randomly generated. There are various differences between the model and control maps. The model map has an irregular shape while the control map is globular. Another difference relates to the respective map diameters: the volume enclosed by map axes is greater for the model than the control. This indicates that dispersion among New Testament texts is greater than would be expected if those texts resulted from random selection among alternative readings. Yet another difference is the proportion of variance figures for the model and control maps, which are respectively 0.51 and 0.16. The dimensionality of the New Testament distance data is lower than for the control data, making it easier to squeeze into only three dimensions.

Divisive clustering begins with a single cluster and ends with individual objects. The R program documentation describes the clustering algorithm as follows:[15]

At each stage, the cluster with the largest diameter is selected. (The diameter of a cluster is the largest dissimilarity between any two of its observations.) To divide the selected cluster, the algorithm first looks for its most disparate observation (i.e., which has the largest average dissimilarity to the other observations of the selected cluster). This observation initiates the "splinter group". In subsequent steps, the algorithm reassigns observations that are closer to the "splinter group" than to the "old party". The result is a division of the selected cluster into two new clusters.

This type of analysis produces a dendrogram which shows the heights at which clusters divide into sub-clusters. A divisive coefficient which measures the amount of clustering is presented as well. The value of this coefficient ranges from zero to one with larger values indicating a greater degree of clustering. A DC dendrogram does not necessarily reflect the family tree of objects in the underlying data set. Instead, it merely shows a reasonable way to progressively subdivide an all-encompassing cluster until every sub-cluster is comprised of a single object.[16]


The vocabulary of tree structures is useful when discussing DC dendrograms. A branching point is called a node, each structure which descends from a node is called a branch, and terminals are called leaves. The dendrograms produced by analysing New Testament data have a self-similar character where, apart from scale, smaller parts have the same appearance as larger parts. Each branch contains its own sub-branches, unless terminated by leaves (i.e. individual witnesses).

A partition based on a DC dendrogram is obtained by means of a horizontal line which cuts across the dendrogram at some height to produce a set of separate branches. One possible height to cut a DC dendrogram is the upper critical limit of distances. Such a large distance is seldom encountered among unrelated objects. Cutting at the upper critical limit produces the following partition of the model data set.[17]


Performing DC analysis on the control distance matrix produces this dendrogram:


The model and control dendrograms seem quite similar at first glance although there are important differences: nearly all of the branching heights in the control dendrogram are in the normal range [0.374, 0.553], and the divisive coefficient for the model (0.74) is much larger than for the control (0.33).

The objects in the control can be grouped even though it is pointless to do so: if nearly all distances between objects fall within the normal range then partitioning may well be futile. In the present case, group sizes are more uniform for the control than model although there is no reason why a data set with actual groups cannot have uniform group sizes.


Neighbour joining (NJ) is an iterative process that begins with a starlike tree. A pair of neighbours is chosen at every step, being that pair of objects which gives the smallest sum of branch lengths. A node is then inserted between this pair, which node is regarded as a single object for subsequent steps. The procedure seeks to find the minimum-evolution tree, being that tree which most economically accounts for the observed set of distances between objects. While the method produces a unique final tree under the principle of minimum evolution, it does not always produce the minimum-evolution tree. However, computer simulations show that it is quite efficient in obtaining the correct tree topology.[18]

As with DC dendrograms, the vocabulary of tree structures is useful for discussing NJ analysis results. The NJ procedure produces an unrooted tree, meaning that any node or terminal in the result could be closest to the common ancestor of the entire tree.

Applying the NJ procedure to the model distance matrix produces a tree whose branches correspond to clusters seen in the CMDS and DC results obtained from the same distance matrix:[19]


The tree obtained from the control distance matrix retains the NJ algorithm's initial starlike structure. This shows what kind of topology (i.e. shape) to expect for an NJ result derived from a data set comprised of unrelated objects. The marked difference from the model result is another indication that clustering exists among texts of Mark's Gospel.


Partitioning around medoids (PAM) builds clusters around representative objects called medoids. The program documentation provides this description:[20]

The ‘pam’-algorithm is based on the search for ‘k’ representative objects or medoids among the observations of the dataset. These observations should represent the structure of the data. After finding a set of ‘k’ medoids, ‘k’ clusters are constructed by assigning each observation to the nearest medoid. The goal is to find ‘k’ representative objects which minimize the sum of the dissimilarities of the observations to their closest representative object.

PAM analysis can be used to divide a data set into any number of clusters between two and the number of cases in the data set. The standard procedure in this study will be to partition data sets into two, three, four, five, and twelve clusters. The progression from two to five shows which groups separate first, while the twelve-way partition is useful for revealing core group members.[21]

Table 7. PAM (Mark, UBS4)

No. Groups with their medoids in [brackets] Singletons Poorly classified (worst last)
2
UBS Aleph B D L W Theta 565 2427 it-a it-aur it-b it-c it-d it-f it-ff-2 it-i it-k it-l it-q it-r-1 [vg] syr-pal syr-s cop-sa cop-bo arm eth geo Augustine A C Delta Psi f-1 f-13 28 33 157 180 205 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav
Theta 2427 arm 565 it-aur L Augustine geo it-l it-q vg it-f syr-pal eth
3
UBS Aleph B C L W Delta [Psi] 892 2427 it-k syr-s cop-sa cop-bo A f-1 f-13 28 33 157 180 205 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect it-aur it-f it-l syr-p syr-h syr-pal eth geo slav Augustine D Theta 565 it-a it-b it-c it-d it-ff-2 [it-i] it-q it-r-1 vg arm
C syr-s arm vg
4
UBS Aleph B C L W Delta [Psi] 892 2427 it-k cop-sa cop-bo A f-1 f-13 28 33 157 180 205 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav D Theta it-a it-b it-d it-ff-2 [it-i] it-r-1 565 it-aur it-c it-f it-l it-q [vg] syr-pal syr-s arm eth geo Augustine
syr-pal C W it-q Theta it-c eth
5
UBS Aleph [B] L Delta Psi 2427 cop-sa cop-bo A C f-13 33 157 180 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav D it-a it-b it-c it-d [it-ff-2] it-i it-k it-r-1 W [f-1] 28 205 565 syr-s arm geo Theta it-aur it-f it-l it-q [vg] syr-pal eth Augustine
eth 892
12
UBS Aleph [B] Psi 2427 A f-13 33 157 180 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav C L [Delta] D it-a [it-d] [Theta] 565 syr-pal f-1 28 [205] 892 cop-sa [cop-bo] it-aur it-c it-f it-l it-q [vg] eth Augustine it-b it-ff-2 [it-i] it-r-1 syr-s [arm] geo
W it-k 892 eth it-c L

Brackets mark the medoid of each group. A medoid has the minimum mean distance to other group members and is the most central one for groups of three or more items. For two member groups, the PAM algorithm chooses one as the medoid.

Note

This study uses the bracketed medoid identifier as a label for the associated group. For example, [vg] refers to the first group in the above two-way partition.

A singleton is a solitary item which forms its own group. It is isolated, not having any close relatives within the data set. Singletons are listed under a separate heading, and the medoid of a singleton group is the sole member itself. The total number of groups in a partition equals the sum of numbers of singletons and multiple member groups.

Not all members of a group need be a good fit. PAM analysis calculates a statistic called the silhouette width for each object in the data set being partitioned into a chosen number of groups. Its value ranges from +1 to -1: the closer it is to +1, the better the associated case fits into its assigned group; by contrast, the closer the statistic is to -1, the worse the fit. Like hammering square pegs into round holes (or vice versa), negative silhouette widths indicate that the affected cases are not well suited to their assigned places. The last column in the table lists witnesses with negative silhouette widths, putting those with the most negative values last. The worst classified witnesses lie farthest to the right in such a list. A poor fit may indicate that a witness has a mixed text or that the chosen number of groups is too small for a text to be grouped with like texts alone.

As a data set is partitioned into larger numbers of groups, parent groups tend to spawn child groups while themselves contracting into narrower, more coherent groups. Group [Byz] is an example: as the same data set is partitioned into more and more groups, this group contributes items to various other groups while retaining a core membership. Partitioning a data set into a large number of groups reveals coherent cores comprised of close confederates.

Adding the partition's number of groups to the group label produces a more specific identifier. For example, [Byz] (3) refers to the group with medoid Byz in a three-way partition while [Byz] (12) refers to the group with medoid Byz in a twelve-way partition. Corresponding groups such as [Byz] (3) and [Byz] (12) are often produced when the same data set is divided into different numbers of parts. However, the medoids of such groups are not necessarily the same. Adding or subtracting even a single member can cause the medoid of a group to change. Consequently, correspondence must be established on the basis of shared membership, not common medoids. If groups from different partitions have the same core membership but differing medoids then descendant groups can be labelled by chaining the respective medoids together. To give an example from the table above, [Psi] (3) and [B] (5) share members but their medoids differ. One might label the subgroup as [Psi-B] (5) to indicate the connection with the supergroup from which its members are drawn.

Some numbers of groups are more suitable than others. Plotting a statistic called the mean silhouette width against each possible number of groups indicates which numbers of groups are more natural for the data set. The plot for the model data set indicates that three, six, eleven, and twenty-four are among the more preferable numbers of groups.[22]


The MSW plot for the control data set also has a number of peaks even though that data set has no actual groups.


Comparing the model and control MSW plots reveals a great difference in the respective magnitudes of the MSW statistic. While the control data set is randomly generated and consequently contains no actual groups, there is nevertheless random clustering which accounts for the peaks seen in the associated MSW plot. The MSW plot for the control data set establishes a noise level: peaks with such small magnitudes are worthless as indicators of grouping.

The readings of a particular text can be subtracted to mask its effect on a data matrix. This is useful in cases where the text in question is thought to contribute readings to other texts. One example is the Byzantine text, which is a component of many mixed texts. If the influence of such a text is removed from a data matrix then what remains can be analysed to see how other texts relate in its absence.

Subtraction is achieved by selecting a text to eliminate then replacing its readings with NA wherever they occur in a data matrix. A script called mask.R performs the task to produce a data matrix in which all traces of the subtracted text are eliminated.

PAM (or a similar technique) can be used to identify texts that have a claim to represent their respective clusters. Once medoids are identified, they can be used to produce a corresponding series of masked data matrices in which the respective texts are eliminated. Such a series is given in the UBS4 data matrices of Mark, above.

This section presents results obtained by analysing the data sets referenced above using the methods described in the preceding section. The results are given in three parts:

PAM results are presented for a series of data sets selected for their broad coverage of witnesses and variation sites in respective sections of the New Testament. For patristic data sets, ranked distance is the preferred analysis mode.

Table 8. CMDS, DC, and NJ

Section Source CMDS DC NJ
Matthew Brooks
CB
Cosaert
Ehrman
INTF-Parallel
Racine
UBS2
UBS4
Wasserman
Mark CB
Cosaert
Hurtado (Mk 1)
Hurtado (Mk 2)
Hurtado (Mk 3)
Hurtado (Mk 4)
Hurtado (Mk 5)
Hurtado (Mk 6)
Hurtado (Mk 7)
Hurtado (Mk 8)
Hurtado (Mk 9)
Hurtado (Mk 10)
Hurtado (Mk 11)
Hurtado (Mk 12)
Hurtado (Mk 13)
Hurtado (Mk 14)
Hurtado (Mk 15+)
Hurtado (P45)
Mullen
Mullen (P45)
INTF-Parallel
UBS2 (C)
UBS4
UBS4 (control)
UBS4 (non-B)
UBS4 (non-Byz)
UBS4 (non-f-1)
UBS4 (non-it-ff-2)
UBS4 (non-vg)
Wasserman
Luke Brooks
CB
Cosaert
Fee (Lk 10)
INTF-Parallel
UBS2
Wasserman
John Brooks (C)
Brooks (it-j)
CB
Cosaert
Cunningham
EFH
Fee (Jn 1-8)
Fee (Jn 1-8, corr.)
Fee (Jn 4)
Fee (Jn 4, corr.)
Fee (Jn 4, pat.)
Fee (Jn 4, pat., corr.)
Fee (Jn 9)
Fee (Jn 9, corr.)
INTF-Parallel
UBS2 (Aleph)
Wasserman
PA Wasserman
Acts Donker
Donker (Acts 1-12)
Donker (Acts 13-28)
Osburn
UBS2 (Aleph)
UBS2 (P45)
General Letters Donker
James INTF-General
1 Peter INTF-General
UBS4
2 Peter INTF-General
1 John INTF-General
Richards
UBS4
2 John INTF-General
3 John INTF-General
Jude INTF-General
Paul's Letters Brooks
Cunningham
Donker
Osburn
Romans Donker
1 Corinthians Donker
2 Corinthians UBS4
2 Cor. - Titus Donker
Hebrews Donker
UBS4
UBS4 (B)
Revelation UBS4

PAM results for selected data sets are presented below, arranged according to major divisions of the New Testament. The chosen data sets have a relatively broad coverage of witnesses and variation sites. Group medoids are marked by brackets (e.g. [033]). Results for each data set are presented as partitions into two, three, four, five, and twelve groups: the progression through two to five reveals the sequence of group emergence; the division into twelve (a somewhat arbitrary number) shows which groups survive a many-way division. (Such groups are aptly described as coherent.)

Table 9. PAM (Matt, INTF-Parallel)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
01 011 013 017 019 021 0211 022 0233 028 03 030 031 032 [033] 034 036 037 04 041 042 043 045 047 05 07 09 1 1009 1012 1071 1093 1110 118 1230 1241 1253 1273 1279 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1500 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 28 3 31 33 348 35 372 4 517 555 565 579 61 700 713 732 735 740 752 79 791 792 807 827 829 851 863 892 954 968 979 A 038 124 13 346 543 69 788 [826] 828 983
33 05 01 892 A 03
3
01 011 013 019 021 0233 03 031 032 [033] 034 036 037 04 042 043 05 07 09 1 1009 1071 1093 118 1253 1273 1279 131 1328 1329 1330 1331 1333 1334 1338 1339 1340 1342 1344 1345 1346 1446 1451 1457 150 1502 1528 1555 1579 1593 1602 1661 1692 174 176 1780 18 1823 191 205 209 22 222 2372 2726 2737 2766 2786 33 348 35 372 4 517 61 700 732 735 740 752 79 791 792 807 827 829 892 968 A 017 0211 022 028 030 041 [045] 047 1012 1110 1230 1241 1296 130 1326 1335 1336 1341 1343 1347 1348 1421 1424 1500 1506 157 1574 1582 16 1604 1675 184 2193 233 2411 2542 2546 2680 273 28 3 31 555 565 579 713 851 863 954 979 038 124 13 346 543 69 788 [826] 828 983
209 118 205 019 79 372 33 1 700 05 2737 01 740 1593 04 732 792 1823 032 043 1009 2766 1446 A 892 735 174 807 2726 827 2786 011 1661 61 2372 037 1342 042 1457 829 1344 1071 1279 22 1328 1093 348 1330 1579 1334 013 1528 4 1780 18 791 1451 1331 1339 1253 03 1692 35 1555 131 222 1273 517 968 176 1502 1340 150 1329 0233 191 021 1333 752 07 031 036 034 1345 1338 1602 1346 09
4
01 011 013 019 021 0233 03 031 032 [033] 034 036 037 04 042 043 05 07 09 1 1009 1071 1093 118 1253 1273 131 1328 1329 1330 1331 1333 1334 1338 1339 1340 1342 1344 1345 1346 1446 1451 1457 150 1502 1555 1593 1602 1661 1692 174 176 1780 18 1823 191 205 209 22 222 2372 2737 2766 2786 33 35 372 4 517 700 732 735 740 752 79 791 792 807 827 892 968 A 017 0211 022 028 030 041 [045] 047 1012 1110 1230 1241 1296 130 1326 1335 1336 1341 1343 1347 1348 1424 1500 1506 157 1574 1582 1604 1675 2193 233 2411 2542 2546 2680 273 28 3 31 565 579 713 851 863 954 979 038 124 13 346 543 69 788 [826] 828 983 1279 1421 1528 1579 16 [184] 2726 348 555 61 829
1582 209 2680 118 205 019 372 79 33 1 700 1424 01 05 2737 04 1593 740 A 032 1823 732 792 043 892 2766 1446 1009 174 735 1661 827 011 807 2786 037 2372 22 042 1457 1328 1342 1344 1071 03 1330 1093 1334 013 18 1780 791 4 1451 1339 1331 1253 35 1692 1555 968 517 176 1273 131 222 1340 1502 0233 021 150 1329 191 1333 07 752 031 036 1345 034 1346 1338 1602 09
5
01 03 038 05 1582 33 892 [A] 011 013 019 021 0233 031 032 [033] 034 036 037 04 042 043 07 09 1 1009 1071 1093 118 1253 1273 131 1328 1329 1330 1331 1333 1334 1338 1339 1340 1342 1344 1345 1346 1446 1451 1457 150 1502 1555 1593 1602 1661 1692 174 176 1780 18 1823 191 205 209 22 222 2372 2737 2766 2786 35 372 4 517 700 732 735 740 752 79 791 792 807 827 968 017 0211 022 028 030 041 [045] 047 1012 1110 1230 1241 1296 130 1326 1335 1336 1341 1343 1347 1348 1424 1500 1506 157 1574 1604 1675 2193 233 2411 2542 2546 2680 273 28 3 31 565 579 713 851 863 954 979 124 13 346 543 69 788 [826] 828 983 1279 1421 1528 1579 16 [184] 2726 348 555 61 829
2680 79 1823 032 732 372 022 011 019 1582 740 700 043 2737 1334 1328 18 04 1009 735 013 1339 1424 174 792 037 1342 827 2766 1457 1330 1446 1344 35 1071 2372 2786 1661 22 042 1780 131 807 791 1093 1340 1692 1451 150 031 968 4 1331 1333 07 1555 1329 021 752 176 1273 1253 222 517 034 1502 0233 191 036 1345 1346 09 1338 1602 1 038
12
01 03 038 33 892 [A] 011 013 019 021 0233 031 032 [033] 034 036 037 04 042 043 07 09 1009 1071 1093 1253 1273 131 1328 1329 1330 1331 1333 1334 1338 1339 1340 1342 1344 1345 1446 1457 150 1502 1555 1593 1692 174 176 1780 18 1823 191 22 222 2372 2786 35 4 700 732 735 740 752 79 791 792 807 827 017 041 1241 1346 1500 1602 1661 [2193] 2411 2766 565 713 0211 022 028 030 [045] 047 1110 1230 1296 130 1326 1335 1336 1341 1343 1347 1348 1506 157 1574 233 2542 2546 2680 273 28 3 31 579 851 863 979 1 [1582] 1012 1451 [968] 118 205 [209] 124 13 346 543 69 788 [826] 828 983 1279 1421 1528 1579 16 [184] 2726 348 555 61 829 1424 1604 1675 [517] 954 2737 [372]
05 740 1336 1330 273 33 3 79 1326 792 979 013 130 827 752 1093 807 1344 1780 1506 0211 1347 1342 2542 022 047 2372 31 4 030 1253 28 735 892 019 1230 1333 579 1341 045 032 131 1334 043 011 1446 1343 2680 233 851 1692 1273 1457 1823 1296 09 1555 150 1574 1071 2546 157 1348 1338 18 1339 031 191 036 2786 22 174 1110 042 037 791 1328 1340 35 07 028 04 176 038 700 222 1331 1345 0233 1329 034 021 1502

Table 10. PAM (Matt, UBS2)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
Aleph B D f-1 it-a [it-aur] it-b it-g-1 it-c it-d it-ff-2 it-l it-q it-h it-ff-1 it-k vg syr-s syr-c cop Cyprian Ambrose Jerome Augustine Cyril Origen Eusebius C [K] L W X Delta Theta Pi Byz Lect f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f syr-p syr-pal syr-h goth arm geo eth Diatessaron Chrysostom Basil Aleph-c
Eusebius it-h f-1 Cyril cop it-q
3
Aleph B f-1 33 892 [cop] C [K] L W X Delta Theta Pi Byz Lect f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f syr-p syr-pal syr-h goth arm geo eth Diatessaron Chrysostom Basil Aleph-c D it-a [it-aur] it-b it-g-1 it-c it-d it-ff-2 it-l it-q it-h it-ff-1 it-k vg syr-s syr-c Cyprian Ambrose Jerome Augustine Cyril Origen Eusebius
syr-pal it-k 33 Cyprian Jerome Eusebius Origen it-q Cyril
4
Aleph B f-1 33 892 [cop] C [K] L W X Delta Pi Byz Lect f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f syr-p syr-pal syr-h goth eth Diatessaron Chrysostom Basil Aleph-c D it-a [it-aur] it-b it-g-1 it-c it-d it-ff-2 it-l it-q it-h it-ff-1 it-k vg syr-c Cyprian Ambrose Jerome Augustine Cyril Eusebius Theta syr-s [arm] geo Origen
it-k Origen syr-pal Cyprian f-1 33 Jerome Eusebius it-q Cyril
5
Aleph B f-1 33 892 [cop] C [K] L W X Delta Pi Byz Lect f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f syr-p syr-pal syr-h goth eth Diatessaron Chrysostom Basil Aleph-c D [it-a] it-b it-d it-ff-2 it-h Cyprian Theta syr-s [arm] geo Origen [it-aur] it-g-1 it-c it-l it-q it-ff-1 it-k vg syr-c Ambrose Jerome Augustine Cyril Eusebius
it-g-1 it-k syr-s Origen syr-pal Jerome f-1 33 it-c Ambrose Eusebius it-q Cyril
12
Aleph 33 [cop] B 892 [Cyril] C [K] L W X Delta Pi Byz Lect f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f syr-p syr-pal syr-h goth eth Chrysostom Basil Aleph-c D [it-d] Theta arm [geo] [f-1] Cyprian Origen it-a [it-b] it-c it-ff-2 it-h Ambrose [it-aur] it-g-1 it-l it-q it-ff-1 vg Jerome Augustine [syr-s] syr-c
it-k Diatessaron Eusebius 33 Cyprian Origen syr-pal it-q 700 892

Table 11. PAM (Matt, UBS4)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
UBS Aleph B D f-1 it-a it-aur it-b it-c it-d it-ff-1 it-g-1 it-k it-l [vg] syr-c cop-sa cop-meg cop-bo Augustine Chromatius Jerome C L W Delta Theta 0233 f-13 28 33 157 180 205 565 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 Byz E G Sigma [Lect] it-f it-h it-q syr-s syr-p syr-h syr-pal arm eth geo slav Chrysostom
syr-s f-1
3
UBS Aleph B it-k syr-c syr-s [cop-sa] cop-meg cop-bo C L W Delta Theta 0233 f-13 28 33 157 180 205 565 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 Byz E G Sigma [Lect] it-f it-h it-q syr-p syr-h syr-pal arm eth geo slav Chrysostom D f-1 it-a it-aur it-b it-c it-d it-ff-1 it-g-1 it-l [vg] Augustine Chromatius Jerome
syr-s it-k cop-meg it-h f-1
4
UBS Aleph B it-k syr-c [cop-sa] cop-meg cop-bo C D Delta Theta [0233] f-13 1241 Sigma it-d syr-s syr-h arm eth geo L W 28 33 157 180 205 565 579 597 700 892 1006 1010 1071 1243 1292 1342 1424 1505 Byz E G [Lect] it-f it-h it-q syr-p syr-pal slav Chrysostom f-1 it-a it-aur it-b it-c it-ff-1 it-g-1 it-l [vg] Augustine Chromatius Jerome
it-q syr-c syr-s arm D cop-meg it-d it-k f-13 eth C f-1 it-h Delta Sigma syr-h 1241
5
UBS Aleph B it-k syr-c [cop-sa] cop-meg cop-bo C D Delta Theta [0233] f-13 1241 Sigma it-d syr-h arm eth geo L W 28 33 157 180 205 565 579 597 700 892 1006 1010 1071 1243 1292 1342 1424 1505 Byz E G [Lect] it-f syr-p syr-pal slav Chrysostom f-1 it-aur it-ff-1 it-l [vg] Augustine Jerome it-a [it-b] it-c it-g-1 it-h it-q syr-s Chromatius
arm D it-g-1 f-1 eth f-13 C Chromatius it-d it-k cop-meg syr-c Delta Sigma syr-h 1241
12
UBS Aleph [B] cop-sa C Delta [0233] f-13 1241 Sigma arm geo D [it-d] L W 157 180 565 579 597 1006 1071 1243 1292 1342 1505 Byz E G [Lect] slav Theta [28] 33 700 892 1010 1424 syr-p syr-h eth Chrysostom f-1 [205] it-a [it-b] it-c it-g-1 it-h Chromatius it-aur it-ff-1 it-l [vg] Augustine Jerome [it-f] it-q syr-pal syr-c [syr-s] cop-meg [cop-bo]
it-k cop-sa it-g-1 syr-pal 892 syr-p Theta eth 33 Chromatius 700 1424 Chrysostom Delta Sigma syr-h 1241 28 1010

Table 12. PAM (Mark, INTF-Parallel)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
01 019 03 037 04 044 05 1342 33 579 892 [A] 011 013 017 02 021 0211 022 028 030 031 032 033 034 036 038 041 042 043 045 047 07 09 1 1009 1012 1071 1093 1110 118 1230 124 1241 1253 1273 1279 1296 13 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 [1339] 1340 1341 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1500 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 28 3 31 346 348 35 372 4 427 517 543 555 565 61 69 700 713 732 740 752 788 79 791 792 807 826 827 828 829 851 863 954 968 979 983
05
3
01 019 03 037 04 044 05 1342 33 579 892 [A] 011 013 017 02 021 0211 022 028 030 031 032 033 034 036 041 042 043 045 047 07 09 1 1009 1012 1071 1093 1110 118 1230 1241 1253 1273 1279 1296 130 131 1326 [1328] 1329 1330 1331 1333 1334 1335 1336 1337 1338 1339 1340 1341 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1500 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 3 31 348 35 372 4 427 517 555 61 700 713 732 740 752 79 791 792 807 827 829 851 863 954 968 979 038 124 13 28 346 543 565 69 788 [826] 828 983
05 032
4
01 019 03 037 04 044 05 1342 33 579 892 [A] 011 013 017 02 021 0211 022 028 030 031 033 034 036 041 042 043 045 047 07 09 1009 1012 1071 1093 1110 1230 1241 1253 1273 1279 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 [1339] 1340 1341 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1500 1502 1506 1528 1555 157 1574 1579 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 2786 3 31 348 35 372 4 427 517 555 61 700 713 732 740 752 79 791 792 807 827 829 851 863 954 968 979 032 1 118 1582 205 [209] 2193 2542 28 038 124 13 346 543 565 69 788 [826] 828 983
05
5
01 019 03 037 04 044 05 1342 33 579 892 [A] 011 013 017 02 021 0211 022 028 030 031 033 034 036 041 042 043 045 047 07 09 1009 1012 1071 1093 1110 1230 1241 1253 1273 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 [1339] 1340 1341 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1500 1502 1506 1555 157 1574 1593 1602 1604 1661 1675 1692 174 176 1780 18 1823 191 22 222 233 2372 2411 2546 2680 273 2737 2766 2786 3 31 35 372 4 427 517 713 732 740 79 791 792 807 827 851 863 954 968 979 032 1 118 1582 205 [209] 2193 2542 28 038 124 13 346 543 69 788 [826] 828 983 1279 [1528] 1579 16 184 2726 348 555 565 61 700 752 829
05 565
12
01 019 03 037 04 044 1342 33 579 892 [A] 011 013 022 028 031 033 034 036 045 047 [07] 09 1009 1071 1093 1110 1273 1296 131 1333 1335 1338 1341 1343 1347 1348 1555 1604 1661 174 176 1823 22 2372 273 2766 2786 3 31 4 700 807 851 017 02 [041] 1346 1421 1500 1602 1780 222 2411 752 021 0211 030 042 043 1012 1230 1253 130 1326 1328 1329 1330 1331 1334 1336 1337 [1339] 1340 1344 1345 1446 1451 1457 150 1502 1506 157 1574 1593 1692 18 191 233 2546 2680 2737 35 372 713 740 79 791 792 827 968 979 038 [565] 1 118 1582 205 [209] 2193 2542 28 124 13 346 543 69 788 [826] 828 983 1241 1424 1675 [517] 954 1279 [1528] 1579 16 184 2726 348 555 61 829 427 [732] 863
032 05 1457 047 1337 1326 1330 176 79 700 1329 2737 1347 1345 1253 372 1574 1692 1009 1823 1338 0211 713 1344 042 157 2546 1451 2766 2680 1230 968 791 31 043 1071 1336 130 1502 233 1012 150 030 752 1340 021 034

Table 13. PAM (Mark, UBS2, C)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
Aleph A B C K L W X Delta Theta Pi Psi [Byz] Lect f-1 f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-aur it-f it-l vg syr-s syr-p syr-pal syr-h cop goth arm geo eth P Dia Aug Orig D it-a it-b it-c [it-d] it-ff-2 it-q it-r-1 it-i
Aug
3
Aleph B C L Delta Psi 33 [892] syr-s cop Orig A K W X Theta Pi [Byz] Lect f-1 f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-aur it-f it-l vg syr-p syr-pal syr-h goth arm geo eth P Dia Aug D it-a it-b it-c [it-d] it-ff-2 it-q it-r-1 it-i
C Orig syr-s 33
4
Aleph B C L Delta Psi 33 [892] cop Orig A K W X Pi [Byz] Lect f-1 f-13 28 565 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 syr-p syr-h goth arm eth P Dia D it-a it-b [it-d] it-ff-2 it-r-1 it-i Theta it-aur it-c it-f it-l it-q [vg] syr-s syr-pal geo Aug
565 C arm syr-s it-c it-f syr-pal Orig geo it-q 33
5
Aleph B C L Delta Psi 33 [892] cop A K X Pi [Byz] Lect f-1 f-13 700 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 syr-p syr-h goth eth P Dia D it-a it-b [it-d] it-ff-2 it-r-1 it-i W Theta 28 [565] syr-s syr-pal arm geo Orig it-aur it-c it-f it-l it-q [vg] Aug
W 28 syr-pal 33
12
[Aleph] B C A K X Pi [Byz] Lect 1009 1010 1071 1230 1242 1253 1344 2148 syr-h goth eth D it-a it-b [it-d] it-ff-2 it-r-1 it-i L Delta Psi 33 [892] cop Theta [565] f-1 f-13 28 700 1079 1195 1216 1241 1365 1546 1646 2174 [P] it-aur it-c it-f it-l it-q [vg] Aug [syr-p] syr-pal Dia [arm] geo
W Orig syr-s B 1241 cop Psi 1216 1546 1365 2174 1195 Dia syr-pal 1079 33 1646

Table 14. PAM (Mark, UBS4)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
UBS Aleph B D L W Theta 565 2427 it-a it-aur it-b it-c it-d it-f it-ff-2 it-i it-k it-l it-q it-r-1 [vg] syr-pal syr-s cop-sa cop-bo arm eth geo Augustine A C Delta Psi f-1 f-13 28 33 157 180 205 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav
Theta 2427 arm 565 it-aur L Augustine geo it-l it-q vg it-f syr-pal eth
3
UBS Aleph B C L W Delta [Psi] 892 2427 it-k syr-s cop-sa cop-bo A f-1 f-13 28 33 157 180 205 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect it-aur it-f it-l syr-p syr-h syr-pal eth geo slav Augustine D Theta 565 it-a it-b it-c it-d it-ff-2 [it-i] it-q it-r-1 vg arm
C syr-s arm vg
4
UBS Aleph B C L W Delta [Psi] 892 2427 it-k cop-sa cop-bo A f-1 f-13 28 33 157 180 205 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav D Theta it-a it-b it-d it-ff-2 [it-i] it-r-1 565 it-aur it-c it-f it-l it-q [vg] syr-pal syr-s arm eth geo Augustine
syr-pal C W it-q Theta it-c eth
5
UBS Aleph [B] L Delta Psi 2427 cop-sa cop-bo A C f-13 33 157 180 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav D it-a it-b it-c it-d [it-ff-2] it-i it-k it-r-1 W [f-1] 28 205 565 syr-s arm geo Theta it-aur it-f it-l it-q [vg] syr-pal eth Augustine
eth 892
12
UBS Aleph [B] Psi 2427 A f-13 33 157 180 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H N Sigma Lect syr-p syr-h slav C L [Delta] D it-a [it-d] [Theta] 565 syr-pal f-1 28 [205] 892 cop-sa [cop-bo] it-aur it-c it-f it-l it-q [vg] eth Augustine it-b it-ff-2 [it-i] it-r-1 syr-s [arm] geo
W it-k 892 eth it-c L

Table 15. PAM (Mark, UBS4, non-B)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
A C Delta f-13 28 33 157 180 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect syr-h slav D L W Theta f-1 205 565 it-a it-aur it-b it-c it-d it-f it-ff-2 it-i it-l it-q it-r-1 [vg] syr-p syr-s cop-sa arm eth geo
f-1 205 eth L syr-p
3
A C Delta f-13 28 33 157 180 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect syr-h slav D it-a it-b it-d [it-ff-2] it-i it-r-1 syr-s cop-sa L W Theta f-1 205 565 it-aur it-c it-f it-l it-q [vg] syr-p arm eth geo
W it-q cop-sa it-c eth L syr-s syr-p
4
A C Delta f-13 33 157 180 579 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect syr-h slav D it-a it-b [it-d] it-ff-2 it-r-1 L it-aur it-c it-f it-i it-l it-q [vg] syr-p eth W Theta f-1 28 205 565 syr-s cop-sa arm [geo]
28 eth it-q it-c cop-sa syr-p L it-i
5
A C f-13 33 157 180 597 700 892 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect syr-h slav D it-a it-b [it-d] it-ff-2 it-r-1 [L] Delta 579 W Theta f-1 28 205 565 syr-s cop-sa arm [geo] it-aur it-c it-f it-i it-l it-q [vg] syr-p eth
it-c syr-p 28 579 cop-sa it-i
12
A f-13 33 157 180 597 1006 1010 1071 1243 1292 1342 1505 [Byz] E F G H Sigma Lect syr-h D it-a [it-d] L [Delta] Theta [565] f-1 [205] 28 syr-s arm [geo] 579 700 892 1241 1424 syr-p eth [slav] it-aur it-c it-f it-l it-q [vg] it-b it-ff-2 [it-i] it-r-1
C W cop-sa f-13 892 1424 syr-p syr-s it-a 700 slav 28 1241


Table 17. PAM (Mark, UBS4, non-f-1)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
UBS Aleph B D L Delta Theta 565 892 2427 it-a it-aur it-b it-c it-d it-f it-ff-2 it-i it-l it-q it-r-1 [vg] syr-p cop-sa cop-bo arm A C W f-13 28 33 157 180 579 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect syr-h eth slav
it-q 892 vg W it-l it-f syr-p
3
UBS Aleph B C L Delta 579 892 1342 2427 it-c vg cop-sa [cop-bo] eth A f-13 28 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect it-f it-l syr-p syr-h slav D W Theta 565 it-a it-aur it-b it-d it-ff-2 it-i it-q [it-r-1] arm
it-aur 579 it-c eth vg
4
UBS Aleph B C L Delta 579 892 1342 2427 it-c vg cop-sa [cop-bo] eth A f-13 28 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect it-f it-l syr-p syr-h slav D W it-a it-aur it-b it-d it-ff-2 it-i it-q [it-r-1] Theta [565] arm
arm 579 eth it-c vg
5
UBS Aleph [B] L 2427 A f-13 28 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect it-f it-l syr-p syr-h slav C Delta 579 892 1342 it-c vg cop-sa [cop-bo] eth D W it-a it-aur it-b it-d it-ff-2 it-i it-q [it-r-1] Theta [565] arm
arm C cop-sa 579 1342 eth 892 vg it-c Delta
12
UBS Aleph [B] 2427 A f-13 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect syr-h slav C L [Delta] D it-a it-b it-d it-ff-2 it-i it-q [it-r-1] Theta [565] 892 1342 cop-sa [cop-bo] eth it-aur it-f it-l [vg] syr-p
W 28 579 it-c arm it-q cop-sa 892 33 1342 eth L

Table 18. PAM (Mark, UBS4, non-it-ff-2)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
UBS Aleph B C D L W Delta Theta 33 565 579 892 2427 it-c syr-s cop-sa [cop-bo] arm eth geo A f-1 f-13 28 157 180 205 597 700 1006 1010 1071 1241 1243 1292 1342 1424 1505 [Byz] E F G H Sigma Lect it-aur it-f it-l it-q vg syr-p syr-h slav
arm geo 565 579 C Theta 892 it-c 33 eth
3
UBS Aleph B L W Delta 33 579 2427 cop-sa [cop-bo] eth geo A f-1 f-13 28 157 180 205 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect syr-h slav C D Theta 565 892 1342 it-aur it-c it-f it-l it-q [vg] syr-p syr-s arm
it-q it-aur it-l Theta arm geo syr-s 579 565 892 it-f vg syr-p C eth 33 1342
4
[UBS] Aleph B L Delta 2427 cop-bo A f-13 28 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect syr-h slav C D 579 892 1342 it-aur it-c it-f it-l it-q [vg] syr-p eth W Theta f-1 205 565 syr-s cop-sa arm [geo]
28 579 vg syr-p 892 C eth 1342 cop-sa
5
[UBS] Aleph B L Delta 2427 cop-bo A f-13 28 33 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect syr-h slav C 579 892 1342 it-aur it-c it-f it-l it-q [vg] syr-p eth W Theta f-1 205 565 syr-s cop-sa arm [geo]
D 28 892 eth 1342 C cop-sa
12
[UBS] Aleph B L 2427 A f-13 157 180 597 700 1006 1010 1071 1241 1243 1292 1424 1505 [Byz] E F G H Sigma Lect syr-h slav Theta [565] f-1 28 [205] 33 579 cop-sa [cop-bo] eth 892 1342 it-aur it-f it-l it-q [vg] syr-p syr-s arm [geo]
C D W Delta it-c 892 700 eth 33 1342


Table 20. PAM (Luke, INTF-Parallel)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
01 011 013 017 019 02 021 0211 022 024 028 03 030 031 032 033 034 036 037 038 039 04 040 041 044 045 047 05 07 09 1 1009 1012 1071 1093 1110 118 1230 1241 1253 1273 1279 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 28 3 31 33 348 [35] 372 4 427 555 565 579 61 700 713 732 735 740 752 79 791 792 807 827 829 851 863 892 954 968 979 A P75 124 13 346 543 69 788 [826] 828 983
579
3
01 019 03 040 1241 579 [A] P75 011 013 017 02 021 0211 022 024 028 030 031 032 033 034 036 037 038 039 04 041 044 045 047 05 07 09 1 1009 1012 1071 1093 1110 118 1230 1253 1273 1279 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 28 3 31 33 348 [35] 372 4 427 555 565 61 700 713 732 735 740 752 79 791 792 807 827 829 851 863 892 954 968 979 124 13 346 543 69 788 [826] 828 983
33 04 892
4
01 019 03 040 1241 579 [A] P75 011 013 017 02 021 0211 022 024 028 030 031 032 033 034 036 037 038 039 04 041 044 045 047 05 07 09 1009 1012 1071 1093 1110 1230 1253 1273 1279 1296 130 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1502 1506 1528 1555 157 1574 1579 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 2786 28 3 31 33 348 [35] 372 4 427 555 565 61 700 713 732 735 740 752 79 791 792 807 827 829 851 863 892 954 968 979 1 118 131 [1582] 205 209 2193 2542 124 13 346 543 69 788 [826] 828 983
05 33 157 04 892
5
01 019 03 040 1241 579 [A] P75 011 013 02 0211 022 028 030 031 034 036 037 038 039 045 047 07 09 1110 1273 1279 1296 130 1326 1331 1335 1340 1341 1343 1344 1347 1348 1424 1528 1555 1579 1593 16 1604 1675 176 1780 184 22 2372 2411 2546 2726 273 [3] 348 4 555 732 740 752 791 792 829 017 021 024 032 033 04 041 044 05 1009 1012 1071 1093 1230 1253 1328 1329 1330 1333 1334 1336 1337 1338 1339 1342 1345 1346 1421 1446 1451 1457 150 1502 1506 157 1574 1602 1661 1692 174 18 1823 191 222 233 2680 2737 2766 2786 28 31 33 [35] 372 427 565 61 700 713 735 79 807 827 851 863 892 954 968 979 1 118 131 [1582] 205 209 2193 2542 124 13 346 543 69 788 [826] 828 983
1012 1451 968 827 033 05 1692 33 044 222 1506 1342 735 79 863 1253 1230 1574 157 024 1337 700 1457 2766 04 1446 1330 1661 954 2680 021 807 032 713 1346 1071 892 1093 1009 041 233 1336 61 851 1338 017 191 2737 1329 979 372 28 1334 565 18 1339 1328 35 1421 31 1333 1602 1502 1823 427 150 174 1345
12
01 019 03 1241 579 [A] P75 011 013 0211 022 028 030 031 034 036 037 039 045 047 07 09 1110 1273 1296 130 1326 1331 1335 1340 1341 1343 1344 1347 1348 1424 1555 1604 1675 176 1780 22 2372 2546 273 [3] 4 732 791 017 02 [041] 1346 1421 2411 021 044 1009 1093 1230 1253 1328 1329 1330 1333 1334 1336 1337 1338 1339 1342 1345 150 1502 1506 1574 1602 1661 1692 174 18 1823 191 222 233 2680 2737 2766 2786 28 31 [35] 372 427 565 61 713 735 807 851 863 954 979 [024] 032 033 038 04 040 1071 157 33 700 79 792 892 1 118 131 [1582] 205 209 2193 2542 [1012] 1451 968 124 13 346 543 69 788 [826] 828 983 1279 1528 1579 16 [184] 2726 348 555 752 829 [1446] 1457 1593 827
05 740 863 024 1337 1574 1230 1241 1692 044 1253 892 807 1342 2680 191 233 1093 954 851 1661 979 1336 1009 713 61 157 28 1338 1329 1339 372 2737 33 1330 1334 18 1502 31 1328 35 427 565 1333 2766 1602 1823 021 150 792 1071 222 174 033 79 700 04 032 1345 040 038

Table 21. PAM (Luke, UBS2)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
P75 Aleph A C K L W X Delta Theta Pi Psi [Byz] Lect f-1 f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f it-q vg syr-c syr-p syr-pal syr-h cop goth arm geo eth Diatessaron Tertullian Cyril Eusebius Basil B D Xi it-a it-aur it-b it-i it-c it-d it-e it-ff-2 [it-l] it-r-1 syr-s Marcion Irenaeus Ambrose Augustine Origen
syr-s Marcion Origen B it-aur Xi
3
P75 Aleph B [L] Xi 1241 syr-pal cop eth Cyril Origen Eusebius A C K W X Delta Theta Pi Psi [Byz] Lect f-1 f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1242 1253 1344 1365 1546 1646 2148 2174 it-f it-q vg syr-c syr-p syr-h goth arm geo Diatessaron Tertullian Basil D it-a it-aur it-b it-i it-c it-d it-e it-ff-2 [it-l] it-r-1 syr-s Marcion Irenaeus Ambrose Augustine
syr-s Marcion Cyril eth Origen it-aur syr-pal Eusebius
4
P75 Aleph B [L] Xi 1241 syr-pal cop eth Cyril Origen A C K W X Delta Theta Pi Psi [Byz] Lect f-1 f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1242 1253 1344 1365 1546 1646 2148 2174 it-f it-q vg syr-c syr-p syr-h goth arm geo Diatessaron Basil [D] it-d Tertullian Augustine Eusebius it-a it-aur it-b it-i it-c it-e it-ff-2 [it-l] it-r-1 syr-s Marcion Irenaeus Ambrose
Cyril it-e Marcion syr-s Origen it-aur Augustine eth syr-pal Tertullian Eusebius
5
P75 Aleph B [L] Xi 1241 syr-pal cop eth Cyril Origen A C K W X Delta Theta Pi Psi [Byz] Lect f-1 f-13 28 33 565 700 892 1009 1010 1071 1079 1195 1216 1230 1242 1253 1344 1365 1546 1646 2148 2174 syr-c syr-p syr-h goth arm geo Diatessaron Basil [D] it-d Augustine Eusebius it-a it-b it-i it-e it-ff-2 [it-l] it-r-1 syr-s Marcion Irenaeus it-aur it-c [it-f] it-q vg Ambrose Tertullian
it-r-1 Cyril syr-s Marcion Origen it-e eth syr-pal Eusebius
12
P75 [B] Origen Aleph [L] Xi 1241 syr-pal cop Cyril A C K X Delta Theta Pi [Byz] Lect f-13 28 33 700 892 1010 1071 1195 1216 1230 1242 1253 1344 1646 2148 2174 syr-h goth Diatessaron [D] it-d Eusebius W Psi f-1 565 1009 1079 1365 1546 [Basil] it-a it-b it-i it-e it-ff-2 [it-l] it-r-1 [it-aur] it-c it-f it-q vg Irenaeus Augustine syr-s [syr-c] syr-p arm [geo] Marcion [Tertullian]
eth Ambrose Psi Eusebius A X Lect 2148 700 Augustine Cyril 1009 K Irenaeus Origen it-c syr-pal Pi

Table 22. PAM (John, INTF-Parallel)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
01 019 02 022 03 032 038 044 05 1 1071 118 1241 1446 1582 205 209 2193 2786 [33] 565 579 69 892 A P66 P75 011 013 017 021 0211 028 030 031 034 036 037 039 041 045 047 07 09 1009 1012 1093 1110 1230 124 1253 1273 1279 1296 13 130 131 1326 1328 1329 1331 1333 1334 1335 1336 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1451 1457 [150] 1502 1528 1555 157 1574 1579 1593 16 1602 1604 1675 1692 174 1780 18 1823 184 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 28 3 31 348 35 4 543 555 61 700 713 732 740 752 788 79 791 792 807 826 827 828 829 851 863 954 968 979 983
1071 2786 02 69 1446 038
3
01 019 02 022 03 032 038 044 05 1 1071 118 1241 1446 1582 205 209 2193 2786 [33] 565 579 892 A P66 P75 011 013 017 021 0211 028 030 031 034 036 037 039 041 045 047 07 09 1009 1012 1093 1110 1230 1253 1273 1279 1296 130 131 1326 1328 1329 1331 1333 1334 1335 1336 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1451 1457 [150] 1502 1528 1555 157 1574 1579 1593 16 1602 1604 1675 1692 174 1780 18 1823 184 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 28 3 31 348 35 4 555 61 700 713 732 740 752 79 791 792 807 827 829 851 863 954 968 979 124 13 543 69 788 [826] 828 983
1071 2786 02 1446 038
4
01 019 022 03 032 044 05 2786 33 579 892 [A] P66 P75 011 013 017 02 021 0211 028 030 031 034 036 037 038 039 041 045 047 07 09 1009 1012 1071 1093 1110 1230 1253 1273 1279 1296 130 131 1326 1328 1329 1331 1333 1334 1335 1336 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 [150] 1502 1528 1555 157 1574 1579 1593 16 1602 1604 1675 1692 174 1780 18 1823 184 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 28 3 31 348 35 4 555 61 700 713 732 740 752 79 791 792 807 827 829 851 863 954 968 979 1 118 1241 [1582] 205 209 2193 565 124 13 543 69 788 [826] 828 983
01 1346 579 038 2411 P66 041 019 2786 02 1071 044 892 33
5
01 019 022 03 032 044 05 2786 33 579 892 [A] P66 P75 011 013 017 02 021 0211 028 030 031 034 036 037 038 041 045 047 07 09 1009 1012 1110 1230 1273 1296 130 131 1326 1328 1329 1331 1333 1334 1335 1336 1338 1339 1340 1341 1342 1343 1345 1346 1347 1348 1421 1424 1451 1457 [150] 1502 1555 157 1574 1593 1602 1604 1675 1692 174 1780 18 1823 191 22 222 233 2372 2411 2546 2680 2726 273 2737 2766 28 3 31 35 4 61 700 713 732 740 752 79 791 807 851 863 954 968 979 039 1071 1093 1253 1279 1344 1446 1528 1579 16 [184] 348 555 792 827 829 1 118 1241 [1582] 205 209 2193 565 124 13 543 69 788 [826] 828 983
1230 1253 01 579 041 2411 038 P66 1071 019 02 2786 1457 044 892 33
12
011 [013] 021 028 031 036 037 045 047 07 09 1110 124 1333 1335 1338 1341 1342 1347 1421 1555 1574 174 1780 233 2546 28 31 732 752 979 017 02 0211 038 041 1009 1012 1230 1253 1273 [1346] 1451 157 2411 2680 807 851 968 019 022 044 1071 1241 2786 [33] 579 892 03 032 [A] P66 P75 030 034 1296 130 131 1326 1328 1329 1331 1334 1336 1339 1340 1343 1345 1348 [150] 1502 1602 1604 1692 18 1823 191 22 222 2372 2726 273 2737 2766 3 35 4 61 700 713 740 79 791 863 039 1093 1279 1344 1528 1579 16 [184] 348 555 792 829 1 118 [1582] 205 209 2193 565 13 543 69 788 [826] 828 983 1424 1593 [1675] 954 [1446] 1457 827
01 05 191 1331 1692 851 1343 740 273 979 863 791 22 1604 1502 1273 1241 124 017

Table 23. PAM (John, UBS2, Aleph)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
P66 P75 Aleph B C D L X [33] it-d it-l syr-s syr-c cop arm geo Nonnus Cyprian Tertullian Cyril Origen W-supp A K Delta Theta Pi Psi 063 [Byz] Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-a it-aur it-b it-c it-e it-f it-ff-2 it-q it-r-1 vg syr-p syr-pal syr-h goth eth Diatessaron Chrysostom Hilary Theodoret Eusebius
syr-s arm geo Cyril X cop
3
P66 P75 Aleph [B] C L Nonnus Cyril Origen W-supp A K Delta Theta Pi Psi [063] Byz Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 syr-p syr-h arm D X 33 it-a it-aur it-b it-c it-d it-e [it-f] it-ff-2 it-l it-q it-r-1 vg syr-s syr-c syr-pal cop goth geo eth Diatessaron Chrysostom Cyprian Hilary Theodoret Tertullian Eusebius
syr-c Tertullian it-aur Diatessaron it-r-1 Hilary Cyprian it-f eth geo 33 Cyril it-q vg syr-pal Eusebius X Chrysostom cop goth Theodoret
4
P66 P75 Aleph [B] C L Nonnus Origen W-supp A K Delta Theta Psi [063] Byz Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1241 1242 1344 1365 1546 2148 syr-h D it-a it-aur it-b [it-c] it-d it-e it-ff-2 it-l it-r-1 vg Hilary X Pi 33 [1230] 1253 1646 2174 it-f it-q syr-s syr-c syr-p syr-pal cop goth arm geo eth Diatessaron Chrysostom Cyprian Theodoret Tertullian Cyril Eusebius
arm Cyprian Diatessaron it-f Tertullian 33 geo syr-c syr-p vg eth it-q Cyril syr-pal X Eusebius Chrysostom 1230 cop goth 1253 Pi 1646 2174 Theodoret
5
P66 P75 Aleph [B] C L Nonnus Origen W-supp A K Delta Theta Psi [063] Byz Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1241 1242 1344 1365 1546 2148 syr-h D [it-d] syr-c X Pi 33 [1230] 1253 1646 2174 it-f it-q syr-s syr-p syr-pal cop goth arm geo eth Diatessaron Chrysostom Cyprian Theodoret Tertullian Cyril Eusebius it-a it-aur it-b [it-c] it-e it-ff-2 it-l it-r-1 vg Hilary
arm syr-c Cyprian syr-s it-f vg Diatessaron 33 geo Tertullian syr-p eth Cyril it-q X syr-pal Eusebius Chrysostom 1230 cop goth 1253 Pi 1646 2174 Theodoret
12
P66 P75 [B] C Origen W-supp Aleph it-l syr-s [Cyprian] A K Theta Psi [063] Lect f-13 565 892 1009 1010 1071 1079 1241 1242 1344 1365 1546 D [it-d] [L] X 33 cop Nonnus Delta [Byz] f-1 28 700 1195 1216 1646 2148 2174 syr-pal syr-h Theodoret Pi [1230] 1253 it-q syr-p goth Chrysostom Cyril Eusebius [it-a] it-b it-ff-2 it-r-1 syr-c Diatessaron Tertullian it-aur it-c it-f [vg] [it-e] Hilary arm [geo]
eth 1071 1546 1242 Aleph X it-b 565 K Diatessaron 1010 Nonnus 1230 892 Origen f-13 syr-p Cyril Lect Tertullian Chrysostom syr-c 1365 it-q 1344 1009 Eusebius cop it-l it-r-1 it-ff-2 goth 1253 Pi


Table 25. PAM (Acts, UBS2, Aleph)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
P74 Aleph [A] B C 33 81 181 1739 it-ar it-d it-l it-r vg cop-sa cop-bo arm eth geo Lucifer Origen D E P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 630 945 1241 1505 1877 2127 2412 2492 2495 it-e it-gig it-h it-p syr-p syr-h Chrysostom Irenaeus
eth it-d it-gig 1739 Origen Lucifer 181
3
P74 Aleph [A] B C 33 81 181 1739 it-ar it-r vg cop-sa cop-bo arm eth geo Lucifer Origen D [it-d] it-gig it-l it-p Irenaeus E P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 630 945 1241 1505 1877 2127 2412 2492 2495 it-e it-h syr-p syr-h Chrysostom
eth it-l 1739 Lucifer Origen 181
4
P74 Aleph [A] B C 33 81 it-ar it-r vg cop-sa cop-bo eth Lucifer Origen D [it-d] it-gig it-l it-p Irenaeus E 181 630 945 [1739] arm geo P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 1241 1505 1877 2127 2412 2492 2495 it-e it-h syr-p syr-h Chrysostom
it-r it-l E eth it-e Lucifer 81 C 33 Origen
5
P74 Aleph [A] B C 33 81 it-r vg cop-sa cop-bo Lucifer D [it-d] it-gig it-l it-p Irenaeus E Psi 614 2412 it-ar it-e it-h syr-p [syr-h] eth Origen P 049 056 0142 [Byz] Lect 88 104 326 330 436 451 629 1241 1505 1877 2127 2492 2495 Chrysostom 181 630 945 [1739] arm geo
syr-p it-l it-r syr-h it-ar 81 Lucifer E Origen 614 2412 Psi C 33
12
P74 Aleph [A] B C 33 81 D [it-d] Irenaeus E [it-e] P 049 056 0142 [Byz] Lect 88 104 326 330 436 451 629 1241 1505 1877 2127 2492 2495 Chrysostom Psi syr-p [syr-h] Origen 181 630 945 [1739] arm geo 614 [2412] it-h it-ar it-l [vg] cop-bo [it-gig] it-r Lucifer
it-p cop-sa eth syr-h 33 Lucifer Origen C it-r cop-bo Psi

Table 26. PAM (Acts, UBS2, P45)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
P45 P74 [A] B C 33 81 181 1739 it-ar it-d it-l it-r vg cop-sa cop-bo arm eth geo Lucifer Origen Aleph-c D E P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 630 945 1241 1505 1877 2127 2412 2492 2495 it-e it-gig it-h it-p syr-p syr-h Chrysostom
it-d eth it-gig 1739 181 Lucifer Origen P45 Aleph-c
3
P45 P74 [A] B C 33 81 181 1739 it-ar it-r vg cop-sa cop-bo arm eth geo Lucifer Origen Aleph-c D [it-d] it-gig it-l it-p E P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 630 945 1241 1505 1877 2127 2412 2492 2495 it-e it-h syr-p syr-h Chrysostom
it-gig eth 1739 Lucifer 181 Origen P45 Aleph-c
4
P45 P74 [A] B C 33 81 it-ar it-r vg cop-sa cop-bo eth Lucifer Origen D [it-d] it-gig it-l it-p E 181 630 945 [1739] arm geo Aleph-c P Psi 049 056 0142 [Byz] Lect 88 104 326 330 436 451 614 629 1241 1505 1877 2127 2412 2492 2495 it-e it-h syr-p syr-h Chrysostom
it-gig E eth it-e 81 C Lucifer P45 Origen 33
5
P45 P74 [A] B C 33 81 it-r vg cop-sa cop-bo Lucifer D [it-d] it-gig it-l it-p E Psi 614 2412 it-ar it-e it-h syr-p [syr-h] arm eth Origen P 049 056 0142 [Byz] Lect 88 104 326 330 436 451 629 1241 1505 1877 2127 2492 2495 Chrysostom 181 630 [945] 1739 geo Aleph-c
syr-p it-e it-gig syr-h C it-ar 81 Lucifer E P45 614 2412 arm Psi 33
12
P45 P74 [A] B C 33 81 D [it-d] E [it-e] P 049 056 0142 [Byz] Lect 88 104 326 330 436 451 629 1241 1505 1877 2127 2492 2495 Chrysostom Psi syr-p [syr-h] Origen 181 630 [945] 1739 Aleph-c 614 [2412] it-h it-ar it-l [vg] cop-bo [it-gig] it-r Lucifer cop-sa [eth] [arm] geo
it-p syr-h Origen P45 cop-bo 33 Psi it-r C

Table 27. PAM (James, INTF-General)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
ECM P74 01 02 03 04 018 020 025 044 049 056 0142 1 5 6 18 33 35 38 43 61 69 81 88 93 94 104 180 181 197 218 252 254 307 312 319 321 322 323 326 330 365 378 398 400 424 431 436 442 453 456 459 467 468 607 [617] 621 623 629 631 642 643 665 676 720 808 876 915 918 945 996 999 1066 1067 1127 1175 1241 1243 1251 1270 1297 1359 1367 1390 1409 1501 1509 1524 1563 1595 1598 1609 1661 1678 1718 1729 1735 1739 1751 1765 1827 1832 1837 1838 1840 1842 1845 1848 1850 1853 1874 1875 1893 2080 2147 2180 2186 2197 2242 2243 2298 2344 2374 2423 2464 2492 2494 2523 2541 2544 2652 2674 2718 2774 2805 2818 206 [429] 522 614 630 1292 1448 1490 1505 1611 1799 1831 1852 1890 2138 2200 2412 2495
2147 2652
3
[ECM] P74 01 02 03 04 025 044 33 81 945 1175 1241 1243 1735 1739 1852 2298 2344 2492 2805 018 020 049 056 0142 1 5 6 18 35 38 43 61 69 88 93 94 104 180 181 197 218 252 254 307 312 319 321 322 323 326 330 365 378 398 400 424 431 436 442 453 456 459 467 468 607 [617] 621 623 629 631 642 643 665 676 720 808 876 915 918 996 999 1066 1067 1127 1251 1270 1297 1359 1367 1390 1409 1501 1509 1524 1563 1595 1598 1609 1661 1678 1718 1729 1751 1765 1827 1832 1837 1838 1840 1842 1845 1848 1850 1853 1874 1875 1893 2080 2147 2180 2186 2197 2242 2243 2374 2423 2464 2494 2523 2541 2544 2652 2674 2718 2774 2818 206 [429] 522 614 630 1292 1448 1490 1505 1611 1799 1831 1890 2138 2200 2412 2495
2805 2147 2492 2652 1852
4
[ECM] P74 01 02 03 04 025 81 945 1175 1241 1243 1739 1852 2298 2344 2805 018 020 049 056 0142 1 5 6 18 35 38 43 61 69 88 93 94 104 180 181 197 218 252 254 307 312 319 321 322 323 326 330 365 378 398 400 [424] 431 442 453 456 459 467 468 607 617 621 623 629 631 642 643 665 676 720 808 876 915 918 996 999 1066 1127 1251 1270 1297 1359 1367 1390 1501 1509 1524 1563 1595 1598 1609 1661 1678 1718 1729 1751 1765 1827 1832 1837 1838 1840 1842 1845 1848 1850 1853 1874 1875 1893 2080 2147 2180 2186 2197 2242 2243 2374 2423 2464 2492 2494 2523 2544 2652 2674 2718 2774 2818 044 33 436 1067 1409 1735 [2541] 206 [429] 522 614 630 1292 1448 1490 1505 1611 1799 1831 1890 2138 2200 2412 2495
2805 2147 2652 02 81 1852 2344
5
[ECM] P74 01 02 03 04 025 81 945 1175 1241 1243 1739 1852 2298 2344 2492 2805 018 020 049 056 0142 1 5 6 18 35 38 43 61 69 88 93 104 181 197 218 252 254 312 319 321 322 323 326 330 365 378 398 400 424 431 442 456 459 467 468 607 [617] 621 623 629 631 642 643 665 676 808 876 915 996 999 1066 1127 1251 1270 1297 1359 1367 1390 1501 1509 1524 1563 1595 1598 1609 1661 1718 1729 1751 1765 1827 1832 1837 1838 1842 1845 1848 1850 1853 1874 1875 1893 2080 2147 2180 2242 2243 2374 2423 2464 2494 2523 2544 2652 2674 2718 2774 044 33 436 1067 1409 1735 [2541] 94 180 [307] 453 720 918 1678 1840 2186 2197 2818 206 [429] 522 614 630 1292 1448 1490 1505 1611 1799 1831 1890 2138 2200 2412 2495
1501 665 319 1729 1850 254 61 2523 43 1359 1874 18 676 2544 1609 378 2423 056 1127 35 1066 6 218 1367 2080 621 1390 93 181 38 2774 631 431 2464 456 808 0142 2147 607 623 1845 5 642 2805 643 365 1875 629 398 1524 1 88 1509 876 312 049 2243 1853 2180 1765 2652 2494 424 1832 81 02 1852 2492 2344 69 018 468
12
[ECM] P74 01 02 03 04 025 81 1175 1243 1852 2344 2492 018 020 049 1 5 6 18 35 38 43 61 69 88 93 104 181 197 252 254 312 319 321 322 323 326 330 365 378 398 424 431 442 456 459 467 468 607 [617] 621 623 629 643 665 676 915 996 999 1251 1367 1390 1501 1509 1524 1609 1661 1729 1751 1827 1837 1838 1842 1845 1848 1850 1853 1874 1875 2080 2242 2423 2523 2544 2674 2718 2774 044 33 436 1067 1409 [2541] [056] 0142 1066 2180 94 180 [307] 453 720 918 1678 1840 2186 2197 2818 206 [429] 522 630 1292 1490 1799 1831 2200 [218] 642 808 1127 1359 1563 1718 1735 2374 2464 2805 400 1270 [1297] 1595 1598 1893 614 1448 [1505] 1611 1890 2138 2412 2495 876 [1765] 1832 2147 2243 2494 2652 945 1241 [1739] 2298
631 2180 181 1367 1875 254 621 1501 1524 81 643 02 424 2544 629 049 88 5 1850 1 1751 398 365 623 915 04 1842 459 2674 2423 104 431 1848 1874 326 1609 61 1735 467 676 1837 607 322 456 2242 1729 2344 2080 1827 6 617 2774 323 252 1390 38 330 1251 1845 312 2492 442 999 18 378 996 43 2523 69 1509 1661 1852 1853 321 020 93 35 2718 319 665 018 468 197 1292

Table 28. PAM (1 Peter, INTF-General)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
[ECM] P81 01 03 621 623 1175 1729 1875 2541 2805 P72 02 04 018 020 025 044 049 [093] 0142 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 206 218 252 254 307 319 321 323 326 330 365 378 398 400 424 429 431 436 442 453 459 467 468 522 607 614 617 629 630 642 665 720 808 876 915 918 945 996 1067 1127 1241 1243 1270 1292 1297 1359 1409 1448 1490 1501 1505 1524 1563 1595 1609 1611 1661 1678 1718 1735 1739 1751 1799 1827 1831 1832 1837 1838 1842 1845 1848 1852 1874 1881 1890 2138 2147 2186 2200 2243 2298 2344 2374 2412 2423 2464 2492 2544 2652 2718 2774 2818
945 2541 2718 1739 5 04 1852 2344 621 044 1243 33 81 P72 1067 1735 436 1409 2464 02 1875 1729
3
[ECM] P81 01 03 621 623 1175 1729 1875 2805 P72 02 04 018 020 025 044 049 [093] 0142 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 206 218 252 254 307 319 321 323 326 330 365 378 398 400 424 429 431 442 453 459 467 468 522 607 614 617 629 630 642 665 720 808 876 915 918 945 996 1127 1241 1243 1270 1292 1297 1359 1448 1490 1501 1505 1524 1563 1595 1609 1611 1661 1678 1718 1735 1739 1751 1799 1827 1831 1832 1837 1838 1842 1845 1848 1852 1874 1881 1890 2138 2147 2186 2200 2243 2298 2344 2374 2412 2423 2464 2492 2544 2652 2718 2774 2818 436 1067 1409 [2541]
1563 1359 945 218 2718 1739 1718 ECM 1852 04 044 1243 01 621 P72 2374 2805 5 2464 33 2344 1875 1735 81 1729 623 02
4
[ECM] P81 01 03 621 623 1175 1729 1875 2805 P72 02 04 018 020 025 044 049 [093] 0142 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 218 252 254 307 319 321 323 326 330 365 378 398 400 424 431 442 453 459 467 468 607 617 629 630 642 665 720 808 876 915 918 945 996 1127 1241 1243 1270 1292 1297 1359 1448 1501 1505 1524 1563 1595 1609 1611 1661 1678 1718 1735 1739 1751 1827 1831 1832 1837 1838 1842 1845 1848 1852 1874 1881 1890 2138 2147 2186 2243 2298 2344 2374 2423 2464 2492 2544 2652 2718 2774 2818 206 [429] 522 614 1490 1799 2200 2412 436 1067 1409 [2541]
2718 945 1739 1832 ECM 876 04 1175 044 1243 1127 01 P72 2374 621 5 2805 1852 378 1448 2464 33 2344 1735 81 1875 1729 2147 623 02 2652 1890 1831 1505 2138 1292 1611 630
5
[ECM] P81 01 03 621 623 1175 1729 1875 2805 P72 02 04 018 020 025 044 049 [093] 0142 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 218 252 254 307 319 321 323 326 330 365 378 398 400 424 431 442 453 459 467 468 607 617 629 630 642 665 720 808 876 915 918 945 996 1127 1241 1243 1270 1292 1297 1448 1501 1505 1524 1595 1609 1611 1661 1678 1735 1739 1751 1827 1831 1832 1837 1838 1842 1845 1848 1852 1874 1881 1890 2138 2147 2186 2243 2298 2344 2374 2423 2464 2492 2544 2652 2718 2774 2818 206 [429] 522 614 1490 1799 2200 2412 436 1067 1409 [2541] [1359] 1563 1718
254 945 424 2423 459 629 1739 43 1881 321 1832 1838 607 1609 2186 1524 93 1595 69 94 ECM 876 915 2718 104 61 04 468 049 35 1842 1175 044 1270 400 307 1243 330 665 2544 2818 1297 01 P72 617 2243 1845 918 453 1837 020 1827 1661 431 88 326 365 6 996 2774 1 1678 1852 5 2805 1751 180 720 319 2492 621 378 1448 2464 33 1735 81 1875 2147 623 1127 02 2344 2652 1729 1890 1831 1505 642 808 2138 2374 1292 1611 218 630
12
ECM P72 02 04 020 025 044 049 [093] 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 218 252 254 307 319 321 323 326 330 365 378 398 400 442 453 459 467 468 607 617 623 629 630 642 665 720 808 915 918 945 996 1127 1241 1243 1270 1292 1297 1448 1501 1505 1524 1595 1609 1611 1661 1678 1735 1739 1751 1827 1837 1838 1842 1845 1848 1852 1874 1881 1890 2138 2147 2186 2298 2344 2374 2423 2464 2492 2652 2718 2774 2818 [P81] 03 018 0142 [424] 431 1729 1875 2544 206 [429] 522 614 1799 2200 2412 436 1067 1409 [2541] 876 [1832] 2243 [1359] 1563 1718 1490 [1831]
01 621 1175 2805 254 1524 307 1881 2818 629 453 918 2298 P72 1838 1661 915 94 1678 323 996 61 2718 88 180 398 720 69 2774 2492 1842 104 1609 1751 459 33 044 365 5 1448 1827 1735 81 1241 2186 400 1595 945 1845 1837 326 468 330 1848 04 1270 1852 467 43 18 1874 378 35 1297 049 2147 252 02 607 2344 665 1127 1501 181 442 321 1739 2652 025 2423 020 6 1243 93 1 319 617 623 2464 1890 ECM 1505 642 808 2374 2138 218 1292 1611 630


Table 30. PAM (2 Peter, INTF-General)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
2
ECM P72 01 02 03 04 018 020 025 044 049 0142 1 5 6 18 33 35 43 61 69 81 88 93 94 104 180 181 218 252 254 307 319 321 323 326 330 365 378 398 400 424 431 436 442 453 459 467 468 607 617 621 623 629 642 [665] 720 808 876 915 918 945 996 1067 1127 1175 1241 1243 1270 1297 1359 1409 1448 1490 1501 1524 1563 1595 1609 1661 1678 1718 1729 1735 1739 1751 1827 1831 1832 1837 1838 1842 1845 1848 1874 1875 1881 1890 2147 2186 2243 2298 2344 2374 2423 2464 2492 2541 2544 2652 2718 2774 2805 2818 206 429 522 614 630 1292 1505 1611 1799 1852 2138 [2200] 2412
254 1524 378 1831 1490 2147 2652
3
[ECM] P72 01 02 03 04 025 044 33 81 307 323 398 436 442 453 467 621 720 915 918 945 996 1067 1175 1241 1243 1661 1678 1739 1848 1852 1881 1890 2147 2298 2344 2541 2652 2718 2805 2818 018 020 049 0142 1 5 6 18 35 43 61 69 88 93 94 104 180 181 218 252 254 319 321 326 330 365 378 400 424 431 459 468 607 617 623 629 642 [665] 808 876 1127 1270 1297 1359 1409 1448 1490 1501 1524 1563 1595 1609 1718 1729 1735 1751 1827 1831 1832 1837 1838 1842 1845 1874 1875 2186 2243 2374 2423 2464 2492 2544 2774 206 429 522 614 630 1292 1505 1611 1799 2138 [2200] 2412
P72 04 2718 2805 1881 398 33 2344 378 1831 81 1490 720 1067 918 2818 436 307 ECM 1678 453 2541 1848 1661 467 996 1852 915 2147 2652 1890
4
[ECM] P72 01 02 03 04 025 044 33 81 323 398 436 442 467 621 915 945 996 1067 1175 1241 1243 1661 1739 1848 1852 1881 1890 2147 2298 2344 2541 2652 2718 2805 018 020 049 0142 1 5 6 18 35 43 61 69 88 93 104 180 181 218 252 254 319 326 330 365 400 424 431 459 468 607 617 623 629 642 [665] 808 876 1127 1270 1297 1359 1409 1448 1490 1501 1524 1563 1595 1609 1718 1729 1735 1751 1827 1831 1832 1837 1838 1842 1845 1874 1875 2186 2243 2374 2423 2464 2492 2544 2774 94 [307] 321 378 453 720 918 1678 2818 206 429 522 614 630 1292 1505 1611 1799 2138 [2200] 2412
1751 1409 02 03 2298 2186 621 1881 2805 P72 33 398 1175 2344 442 01 1831 1243 1490 468 2718 81 025 1067 04 436 2541 1852 1848 1661 467 996 ECM 915 1890 2147 2652
5
[ECM] P72 01 02 03 04 025 044 33 81 323 398 442 467 621 915 945 996 1175 1241 1243 1661 1739 1848 1852 1881 1890 2147 2298 2344 2652 2718 2805 018 020 049 0142 1 5 6 18 35 43 61 69 88 93 104 180 181 218 252 254 319 326 330 365 400 424 431 459 468 607 617 623 629 642 [665] 808 876 1127 1270 1297 1359 1448 1490 1501 1524 1563 1595 1609 1718 1729 1735 1751 1827 1831 1832 1837 1838 1842 1845 1874 1875 2186 2243 2374 2423 2464 2492 2544 2774 94 [307] 321 378 453 720 918 1678 2818 206 429 522 614 630 1292 1505 1611 1799 2138 [2200] 2412 436 1067 1409 [2541]
623 621 2186 1175 P72 1881 398 442 1243 2805 01 1831 1490 468 044 2718 025 1735 81 04 2344 33 02 1852 1848 1661 ECM 996 467 915 1890 2147 2652
12
[ECM] P72 01 02 03 04 025 33 81 398 442 621 1175 1243 1848 2718 018 0142 43 61 93 104 180 252 365 400 424 431 459 468 607 617 642 [665] 876 1127 1270 1501 1595 1609 1735 1751 1832 1837 1838 1842 1875 2243 2423 2492 2774 020 049 1 6 18 35 69 88 181 319 326 330 467 629 915 996 1297 1448 1661 1729 1827 1845 1874 1890 2186 2544 044 5 [623] 2464 2805 94 [307] 321 453 720 918 1678 2818 206 429 522 630 1292 1505 1611 1799 2138 [2200] [218] 808 1359 1563 1718 2344 2374 254 [1524] 1852 323 945 1241 [1739] 1881 2298 378 614 2147 2412 [2652] 436 1067 1409 [2541] 1490 [1831]
1175 1448 2186 43 1270 1751 180 61 2774 018 365 1837 2492 1595 1609 93 424 252 617 1243 01 1127 1875 621 468 1735 2718 025 04 398 442 642 02 33 81 ECM 1848














Ranked distance results are presented in tables devoted to individual Church Fathers. The lists include asterices to mark distances that are not statistically significant provided the number of well-defined variation sites per witness is known. (The numbers, if known, are found in a "counts" file associated with the relevant distance matrix.) Indications of statistical significance are not given when these numbers are not known.

Table 45. Athanasius

Section Ranked distances
Acts (Donker) 81 (0.220); B (0.244); 1891 (0.244); A (0.267*); 044 (0.267*); 01 (0.289*); 630 (0.289*); 1175 (0.289*); 1704 (0.289*); 945 (0.311*); 1739 (0.311*); P74 (0.326*); P (0.341*); C (0.366*); H (0.400*); 1073 (0.400*); L (0.406*); 049 (0.422*); 1352 (0.422*); 383 (0.476*); E (0.489*); 614 (0.511*); D (0.611)
General Letters (Donker) L (0.500*); 105 (0.500*); 201 (0.500*); 1739 (0.500*); A (0.583*); 044 (0.583*); 1022 (0.583*); 1424 (0.583*); 2423 (0.583*); 049 (0.667*); 323 (0.667*); 01 (0.750*); B (0.833)
Paul's Letters (Donker) C (0.322); P (0.378*); A (0.387*); 33 (0.387*); 01-c (0.399*); 01 (0.405*); 1739 (0.417*); 104 (0.419*); L (0.443*); 223 (0.448*); 044 (0.452*); P46 (0.484*); 049 (0.485*); K (0.486*); 876 (0.488*); B (0.496*); 2423 (0.500*); D (0.554); G (0.581); F (0.605)
Romans (Donker) A (0.333*); C (0.368*); 01 (0.381*); 01-c (0.381*); 044 (0.381*); L (0.429*); P (0.429*); 049 (0.429*); 223 (0.429*); 33 (0.476*); 876 (0.476*); 1739 (0.476*); 2423 (0.476*); 104 (0.524*); K (0.529*); D (0.571*); G (0.571*); B (0.619)
1 Corinthians (Donker) A (0.368*); 33 (0.375*); C (0.424*); 01 (0.425*); P46 (0.429*); P (0.474*); 01-c (0.475*); 1739 (0.475*); 044 (0.500*); L (0.525*); B (0.550*); 104 (0.550*); 049 (0.571*); D (0.575*); 223 (0.600); 2423 (0.600); F (0.605); G (0.605); 876 (0.650)
2 Cor. - Titus (Donker) C (0.220); 104 (0.357*); P (0.362*); 223 (0.366*); 01-c (0.394*); K (0.394*); 33 (0.408*); 876 (0.408*); 1739 (0.408*); 2423 (0.412*); L (0.414*); 01 (0.423*); 044 (0.437*); B (0.446*); A (0.450*); 049 (0.460*); D (0.521*); G (0.571); P46 (0.596); F (0.600)
Hebrews (Donker) P (0.278*); 33 (0.306*); 01-c (0.333*); A (0.333*); 104 (0.333*); 1739 (0.333*); 01 (0.361*); P46 (0.389*); B (0.417*); L (0.417*); 223 (0.452*); 044 (0.472*); 876 (0.472*); 2423 (0.528*); K (0.556*); D (0.583*)







Table 52. Gregory of Nyssa

Section Ranked distances
Matt (Brooks) V (0.310*); 1241 (0.330*); E (0.340*); U (0.350*); Omega (0.350*); 1 (0.350*); 33 (0.350*); 544 (0.350*); Sigma (0.360*); Phi (0.360*); 157 (0.360*); C (0.370*); K (0.370*); S (0.370*); Maj (0.370*); 1604 (0.380*); 28 (0.390*); 892 (0.390*); W (0.410); Pi (0.410); UBS (0.410); 565 (0.420); 13 (0.430); 1424 (0.430); L (0.440); 700 (0.440); B (0.450); Aleph (0.470); D (0.490); it-c (0.510); Theta (0.520); it-a (0.540); it-b (0.600)
Matt (Racine) Sigma (0.339); C (0.360*); Delta (0.364*); Omega (0.386*); 33 (0.392*); E (0.393*); L (0.404*); Basil (0.407*); f-1 (0.411*); W (0.421*); Pi (0.439); f-13 (0.439); 565 (0.439); it-e (0.444*); 700 (0.446); Aleph (0.473); B (0.518); Theta (0.518); D (0.562); it-k (0.583); it-a (0.667); it-b (0.739)
Luke (Brooks) 700 (0.260*); C (0.290*); K (0.290*); 33 (0.290*); 544 (0.290*); E (0.300*); 1 (0.300*); S (0.310*); 892 (0.310*); 1241 (0.310*); 1424 (0.310*); Maj (0.310*); V (0.320*); 28 (0.320*); Pi (0.330*); Omega (0.330*); 565 (0.330*); A (0.340*); U (0.340*); Psi (0.340*); UBS (0.340*); 13 (0.350*); P75 (0.360*); 1604 (0.370); Aleph (0.380); L (0.380); 157 (0.400); it-b (0.410); B (0.420); Theta (0.420); it-a (0.420); it-e (0.450); it-c (0.470); W (0.480); D (0.550)
John (Brooks; C) 892 (0.220*); 544 (0.240*); 28 (0.250*); 1604 (0.250*); 1424 (0.260*); V (0.270*); Omega (0.270*); 157 (0.270*); 565 (0.270*); E (0.280*); S (0.280*); Maj. (0.280*); 1241 (0.290*); U (0.300*); Pi (0.300*); 700 (0.300*); K (0.320*); Psi (0.320*); 13 (0.320*); 33 (0.320*); P75 (0.330*); A (0.330*); 1 (0.330*); Theta (0.350*); C (0.360*); L (0.380*); UBS (0.380); P66 (0.390*); it-c (0.410*); Aleph (0.450); B (0.470); it-a (0.470); it-e (0.500); it-b (0.510); W (0.530); D (0.630)
Paul's Letters (Brooks) 075 (0.270*); 0142 (0.270*); P (0.280*); Psi (0.280*); 056 (0.280*); Maj (0.280*); 049 (0.290*); 223 (0.290*); 0150 (0.310*); L (0.320*); 0151 (0.320*); 2423 (0.320*); K (0.330*); C (0.360*); UBS (0.360*); Aleph (0.370*); A (0.370*); 33 (0.370*); 1739 (0.440); B (0.450); P46 (0.490); G (0.570); D (0.600)


This discussion focusses on two categories of analysis results given above:

An attempt will be made to cover what seem to be the most important features of the textual landscape revealed by analysis of the more comprehensive data sets. Analysis results for patristic texts will be discussed as well even though the associated data sets tend not to have a broad coverage of witnesses or variation sites.

Discussion typically begins with PAM results for a data set, though CMDS, DC, NJ, and ranked distance results are often covered as well. Patristic data sets are an exception to this general approach. Due to the underlying vagaries of this hard won class of information, the pictures produced by most of the analysis techniques tend to be uninformative. To give an example of the difficulties encountered, PAM analysis often isolates a patristic witness as a singleton as soon as the number of groups is increased beyond a few. Ranked distances provide a viable alternative in these circumstances, allowing some sense of the textual nature of a patristic witness to be gained by identifying its near neighbours. However, the accuracy and precision of the impressions thus obtained may be open to question due to the inherent uncertainties associated with establishing a Church Father's text from quotations.

A few warnings need to be issued before launching into the disquisition. The results are provisional in the sense that any change to the inputs will produce changes in the outputs. If more comprehensive data sets are analysed then their results will vary from those presented here. The important question is, how much will they vary? If the data set analysed here is already quite comprehensive and is not marred by some form of systematic bias then the corresponding results can be expected to be consistent with any obtained from more comprehensive data sets.[23]

Each analysis method presents its own view of the textual landscape occupied by New Testament witnesses. While these views are generally consistent, there are cases where the analysis modes differ with respect to the alignments of particular witnesses. If this happens then due caution should be exercised when drawing conclusions about the textual complexions of those witnesses. For analysis methods which divide texts into branches or groups (i.e. DC, NJ, and PAM), any text which shares characteristics of multiple branches or groups (sometimes called a "mixed" text) is prone to jump from one place to another if even slight changes are made to the data upon which the analysis is based. The respective analysis techniques will often agree on the core members of a group or branch but disagree on the placement of peripheral ones.

All this raises the question of how best to describe a witness when the different analysis modes present conflicting views of its affiliations. The ranked distance result acts as an arbiter in such cases, providing a standard against which to assess aspects of textual relationships indicated by the respective modes of analysis.[24]



  • Key texts to consider when recovering initial text based on NJ result: 700, 892, Theta, Family 13, Peshitta Syriac, Diatessaron.

  • NJ result indicates that Syriac (except Harclean and Palestinian), Armenian, Georgian, and Latin occupy the same branch which is devoid of Greek MS support (except D).

  • 21-way partition has a cluster (W, Psi, Family 1, 565, 1009, 1079, 1365, 1546) centred on Basil (Cappadocia, d. 379). Did the writings of Church Fathers interfere with the biblical text? Monks might be expected to use theologically-correct phrases when copying (cf. Ehrman). Some members of this cluster are in Streeter's Caesarean group in Mark.

  • D, it-d, and Eusebius associate in 21-way partition. This suggests a link between the D-text and the text used by Eusebius. Is this a clue to the provenance of Codex Bezae? (Spelling analysis would be interesting.)


Table 56. UBS2 John PAM

No. Groups and their [medoids] Singletons Poorly classified (worst last)
3
P66 P75 B C L X [33] syr-c cop arm geo Nonnus Cyprian Tertullian Cyril Origen Aleph-c W-supp A K Delta Theta Pi Psi 063 [Byz] Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1230 1241 1242 1253 1344 1365 1546 1646 2148 2174 it-f it-q syr-p syr-pal syr-h goth eth Chrysostom Theodoret D it-a it-aur it-b [it-c] it-d it-e it-ff-2 it-l it-r-1 vg syr-s Diatessaron Hilary Eusebius
arm syr-c Tertullian geo Cyril vg X Eusebius cop Aleph-c
5
P66 P75 [B] C L Nonnus Origen W-supp A K Delta Theta Psi [063] Byz Lect f-1 f-13 28 565 700 892 1009 1010 1071 1079 1195 1216 1241 1242 1344 1365 1546 2148 syr-h [D] it-d syr-c X Pi 33 [1230] 1253 1646 2174 it-f it-q syr-s syr-p syr-pal cop goth arm geo eth Diatessaron Chrysostom Cyprian Theodoret Tertullian Cyril Eusebius Aleph-c it-a it-aur it-b [it-c] it-e it-ff-2 it-l it-r-1 vg Hilary
arm syr-c Cyprian syr-s it-f vg geo Diatessaron 33 syr-p Tertullian eth Cyril it-q X syr-pal Eusebius Chrysostom 1230 cop goth Aleph-c 1253 Pi 1646 2174 Theodoret
19
P66 P75 [B] C Origen W-supp A K Theta Psi [063] Byz Lect f-13 565 700 1010 1071 1079 1230 1241 1242 1344 1365 [D] it-d L [cop] X [33] Nonnus Aleph-c [Delta] 28 1253 2174 syr-p goth Chrysostom Pi f-1 892 1009 1195 1216 1546 1646 2148 syr-h [Theodoret] [it-a] it-b it-ff-2 it-r-1 Tertullian it-aur it-c [vg] it-e [Hilary] [it-f] it-q it-l syr-s [Cyprian] arm [geo]
syr-c syr-pal eth Diatessaron Cyril Eusebius 1010 Lect 1071 syr-p 700 W-supp 1365 1230 X goth f-13 1344 Tertullian Origen K Chrysostom Byz it-r-1 it-l 1079 1253 28 Nonnus Aleph-c it-ff-2 2174

  • ...

  • ...

  • ...



PAM partitions of INTF Parallel Pericopes data set can be compared with classifications proposed by (1) von Soden and (2) Wisse.

Table 59. PAM (Luke, INTF-Parallel)

No. Groups and their [medoids] Singletons Poorly classified (worst last)
3
01 019 03 040 1241 579 [A] P75 011 013 017 02 021 0211 022 024 028 030 031 032 033 034 036 037 038 039 04 041 044 045 047 05 07 09 1 1009 1012 1071 1093 1110 118 1230 1253 1273 1279 1296 130 131 1326 1328 1329 1330 1331 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1421 1424 1446 1451 1457 150 1502 1506 1528 1555 157 1574 1579 1582 1593 16 1602 1604 1661 1675 1692 174 176 1780 18 1823 184 191 205 209 2193 22 222 233 2372 2411 2542 2546 2680 2726 273 2737 2766 2786 28 3 31 33 348 [35] 372 4 427 555 565 61 700 713 732 735 740 752 79 791 792 807 827 829 851 863 892 954 968 979 124 13 346 543 69 788 [826] 828 983
33 04 892
11
01 019 03 1241 579 [A] P75 011 013 0211 022 028 030 031 034 036 037 039 045 047 07 09 1110 1273 1296 130 1326 1331 1335 1340 1341 1343 1344 1347 1348 1424 1555 1604 1675 176 1780 22 2372 2546 273 [3] 4 732 740 791 017 02 [041] 1346 1421 2411 021 044 1009 1093 1230 1253 1328 1329 1330 1333 1334 1336 1337 1338 1339 1342 1345 150 1502 1506 1574 1602 1661 1692 174 18 1823 191 222 233 2680 2737 2766 2786 28 31 [35] 372 427 565 61 713 735 807 851 863 954 979 [024] 032 033 038 04 040 1071 157 33 700 79 792 892 1 118 131 [1582] 205 209 2193 2542 [1012] 1451 968 124 13 346 543 69 788 [826] 828 983 1279 1528 1579 16 [184] 2726 348 555 752 829 [1446] 1457 1593 827
05 044 1337 1241 1692 807 1342 1253 233 2680 954 892 851 61 979 713 1336 1093 1661 191 1329 1009 1339 1338 28 18 1334 1328 35 372 2737 31 157 1502 427 565 1333 33 1330 1602 1823 2766 150 174 021 792 1071 222 79 033 700 1345 04 032 040 038
41
011 013 0211 022 028 030 031 034 036 037 039 045 047 07 09 1110 1273 1296 130 1326 1331 1335 1340 1341 1343 1344 1347 1348 1555 1780 22 2372 273 [3] 4 732 791 017 02 [041] 1346 1421 2411 019 [040] 33 021 044 1230 1328 1329 1333 1334 1336 1338 1339 1345 150 1502 1506 1574 1602 174 18 1823 191 233 2737 2766 2786 28 31 [35] 372 427 565 61 713 735 807 851 954 979 [024] 032 [03] A P75 1 118 131 [1582] 205 209 2193 [1012] 1451 968 124 13 346 543 69 788 [826] 828 983 [1279] 1579 184 2726 348 555 752 829 1424 [1675] [1446] 1457 1593 827 1528 [16] 1604 [2546]
01 033 038 04 05 1009 1071 1093 1241 1253 1330 1337 1342 157 1661 1692 176 222 2542 2680 579 700 740 79 792 863 892 044 28 150 1502 427 565 1823 1602 31 2766 021 791 1333 1506 174 1345 191 954

  • [826] (i.e. Family 13) is highly coherent, hardly changing as the data set is partitioned into more groups. A two-way partition of this data set separates Family 13 from the rest.

  • Streeter classifies some members of [024] (11) (i.e. 032, 038, 157, 700, 1071) as Caesarean and others as Alexandrian (i.e. 04, 033, 040, 33, 892). Wisse classifies a number of [024] (11) members into his B group for one or more of his test passages (i.e. 032, 040, 33, 157, 700, 892).


Note

The comparison is based on Wisse's table of profile classifications.[26] Each PAM group is labelled by its medoid (e.g. [A], which stands for the Ausgangstext, not Codex Alexandrinus). Figures in parentheses (e.g. 3/4) give the proportion of witnesses in a PAM group that von Soden or Wisse place together in one of their groups. E.g. in the [1446] row, of four witnesses in the PAM group whose classifications by von Soden are given, three are in his Iκ group. In the von Soden column, I and A categories count all witnesses in the corresponding subgroups. In some cases, however, subgroups are specifically listed. For figures given in the Wisse column, each witness is assigned the majority classification across Wisse's three test passages (i.e. Luke chapters 1, 10, and 20). A witness is not counted if there is no majority classification. For von Soden and Wisse columns, entries are made only for those categories which include more than one witness from the corresponding PAM group.

  • In an 11-way partition, PAM groups [A], [041], [1582], [1012], [826], and [1446] correlate well with groups identified by von Soden and Wisse while PAM groups [3], [35], [024], and [184] do not. Group [184] would be a good match if compared with combinations of (1) von Soden's Iβ and Iφ and (2) Wisse's groups 16 and 1216.

The analysis results presented above highlight variations between witnesses of the New Testament. This naturally raises the question of what difference the variations make to the meaning of the text. Most variations are of little consequence — whether an added or dropped article, change of word order, or substitution of a synonymous phrase. Other variations have larger semantic effect, the two most extreme examples being Mark 16.9-20 and John 7.53-8.11 that are absent from a number of witnesses.

One way to convey how much difference the variations make is to provide translations of a number of textual varieties for the same section of text. The following parallel translation of four varieties of the first chapter of Mark highlights the variation sites given in the United Bible Societies Greek New Testament. This edition only presents a selection of textual variations:

The variation sites presented in the UBS apparatus constitute a small proportion of the total number that exist. Nevertheless, those presented are the most important from a semantic point of view, and the great majority of variations not presented in the UBS apparatus have negligible semantic effect. Consequently, looking at these variation sites should give a reliable impression of the magnitude of difference in meaning between textual varieties.

The textual varieties shown in the parallel translation consist of four clusters identified by reference to the DC dendrogram of the UBS4 data set for Mark:[28]

  • A: The mainly Byzantine cluster comprised of A ... syr-pal

  • B: Aleph B C L Delta Psi 892 1342 cop-bo cop-sa it-k

  • C: W Theta f-1 28 205 565 arm geo syr-s

  • D: D it-a it-b it-c it-d it-ff-2 it-i it-q it-r-1

For each variation unit, the reading supported by a textual variety is taken to be the one that occurs most frequently among its members. To illustrate, suppose that a variation unit has three readings and that two witnesses in cluster C have the first, three have the second, and four have the third. The reading supported by cluster C would then be taken to be the third. For the purpose of this exercise, if a tie occurs then the supported reading is taken to be the one with the greatest tendency to isolate the variety.

The highlighted passages show how much the more important differences typically encountered in the New Testament affect the meaning of the text. Many of them are hardly worth a second thought, though a few do convey a different shade of meaning. If this one chapter is representative (and there is no reason to think it is not) then it is fair to say that textual variation in the New Testament has little semantic effect. That said, there are a few places (such as Mark 16.9-20) where the variations are significant.

Table 61. Four-way parallel translation of Mark chapter one

Reference A B C D
1.1 The beginning of the good news about Jesus Christ, Son of God. The beginning of the good news about Jesus Christ, Son of God. The beginning of the good news about Jesus Christ. The beginning of the good news about Jesus Christ, Son of God.
1.2 As written in the prophets, "Look, I send my messenger before you, who will prepare your way;" As written in the prophet Isaiah, "Look, I send my messenger before you, who will prepare your way;" As written by Isaiah the prophet, "Look, I send my messenger before you, who will prepare your way;" As written in the prophet Isaiah, "Look, I send my messenger before you, who will prepare your way;"
1.3 "A voice shouting in the wilderness, 'Prepare the way of the Lord! Make his paths straight!'" "A voice shouting in the wilderness, 'Prepare the way of the Lord! Make his paths straight!'" "A voice shouting in the wilderness, 'Prepare the way of the Lord! Make his paths straight!'" "A voice shouting in the wilderness, 'Prepare the way of the Lord! Make his paths straight!'"
1.4 John appeared, baptizing in the wilderness and announcing a baptism of a changed attitude for forgiveness of wrong deeds. John the Baptist appeared in the wilderness, and [was] announcing a baptism of a changed attitude for forgiveness of wrong deeds. John the Baptist appeared in the wilderness, and [was] announcing a baptism of a changed attitude for forgiveness of wrong deeds. John appeared in the wilderness, baptizing and announcing a baptism of a changed attitude for forgiveness of wrong deeds.
1.5 They went out to him, all of the land of Judea and those of Jerusalem, and were baptized by him, confessing their wrong deeds. They went out to him, all of the land of Judea and those of Jerusalem, and were baptized by him, confessing their wrong deeds. They went out to him, all of the land of Judea and those of Jerusalem, and were baptized by him, confessing their wrong deeds. They went out to him, all of the land of Judea and those of Jerusalem, and were baptized by him, confessing their wrong deeds.
1.6 John was clothed [with] camel hair and a leather covering around his waist; he ate locusts and wild honey. John was clothed [with] camel hair and a leather covering around his waist; he ate locusts and wild honey. John was clothed [with] camel hair and a leather covering around his waist; he ate locusts and wild honey. John was clothed [with] camel hair and a leather covering around his waist; he ate locusts and wild honey.
1.7 He gave notice saying, "One more powerful than me comes after me, whose sandal straps I am not worthy to bend down and untie." He gave notice saying, "One more powerful than me comes after me, whose sandal straps I am not worthy to bend down and untie." He gave notice saying, "One more powerful than me comes after me, whose sandal straps I am not worthy to bend down and untie." He gave notice saying, "I baptize you in water. One more powerful than me comes after me, whose sandal straps I am not worthy to bend down and untie."
1.8 "I baptize you in water; he will baptize you in the Holy Spirit." "I baptize you [in] water; he will baptize you in the Holy Spirit." "I baptize you in water; he will baptize you in the Holy Spirit." "He will baptize you in the Holy Spirit."
1.9 In those days Jesus came from Nazareth, Galilee, and was baptized in the Jordan by John. In those days Jesus came from Nazareth, Galilee, and was baptized in the Jordan by John. In those days Jesus came from Nazareth, Galilee, and was baptized in the Jordan by John. In those days Jesus came from Nazareth, Galilee, and was baptized in the Jordan by John.
1.10 Then coming up from the water he saw the heavens being torn open and the Spirit coming down to him like a dove. Then coming up from the water he saw the heavens being torn open and the Spirit coming down to him like a dove. Then coming up from the water he saw the heavens being torn open and the Spirit coming down to him like a dove; Then coming up from the water he saw the heavens being torn open and the Spirit coming down to him like a dove;
1.11 There came from the heavens a voice: "You are my beloved Son; I am delighted with you." There came from the heavens a voice: "You are my beloved Son; I am delighted with you." from the heavens he heard a voice: "You are my beloved Son; I am delighted with you." from the heavens a voice: "You are my beloved Son; I am delighted with you."
1.12 Then the Spirit drives him into the wilderness. Then the Spirit drives him into the wilderness. Then the Spirit drives him into the wilderness. Then the Spirit drives him into the wilderness.
1.13 He was in the desert forty days being tested by Satan; he was with the wild animals and the angels waited on him. He was in the desert forty days being tested by Satan; he was with the wild animals and the angels waited on him. He was in the desert forty days being tested by Satan; he was with the wild animals and the angels waited on him. He was in the desert forty days being tested by Satan; he was with the wild animals and the angels waited on him.
1.14 After John had been arrested, Jesus went into Galilee announcing the good news of the kingdom of God After John had been arrested, Jesus went into Galilee announcing the good news of God After John had been arrested, Jesus went into Galilee announcing the good news of God After John had been arrested, Jesus went into Galilee announcing the good news of the kingdom of God
1.15 saying, "The time has come and God's kingdom is near. Change your attitude and believe the good news." saying, "The time has come and God's kingdom is near. Change your attitude and believe the good news." saying, "The time has come and God's kingdom is near. Change your attitude and believe the good news." saying, "The time has come and God's kingdom is near. Change your attitude and believe the good news."
1.16 Passing by the Sea of Galilee he saw Simon and Andrew, Simon's brother, throwing a net into the sea. (They were fishermen.) Passing by the Sea of Galilee he saw Simon and Andrew, Simon's brother, throwing a net into the sea. (They were fishermen.) Passing by the Sea of Galilee he saw Simon and Andrew, Simon's brother, throwing a net into the sea. (They were fishermen.) Passing by the Sea of Galilee he saw Simon and Andrew, Simon's brother, throwing nets into the sea. (They were fishermen.)
1.17 Jesus said to them, "Come with me and I will make you into fishers of men." Jesus said to them, "Come with me and I will make you into fishers of men." Jesus said to them, "Come with me and I will make you into fishers of men." Jesus said to them, "Come with me and I will make you into fishers of men."
1.18 Then they left the nets and followed him. Then they left the nets and followed him. Then they left the nets and followed him. Then they left the nets and followed him.
1.19 Going a bit further he saw Jacob Zebedee and his brother John who were in the boat fixing the nets. Going a bit further he saw Jacob Zebedee and his brother John who were in the boat fixing the nets. Going a bit further he saw Jacob Zebedee and his brother John who were in the boat fixing the nets. Going a bit further he saw Jacob Zebedee and his brother John who were in the boat fixing the nets.
1.20 Then he called them. Leaving their father Zebedee in the boat with the hired hands, they went after him. Then he called them. Leaving their father Zebedee in the boat with the hired hands, they went after him. Then he called them. Leaving their father Zebedee in the boat with the hired hands, they went after him. Then he called them. Leaving their father Zebedee in the boat with the hired hands, they went after him.
1.21 They go into Capernaum. Then, on the Sabbath, having gone into the synagogue, he taught. They go into Capernaum. Then, on the Sabbath, having gone into the synagogue, he taught. They go into Capernaum. Then, on the Sabbath, having gone into the synagogue, he taught. They go into Capernaum. Then, on the Sabbath, having gone into the synagogue, he taught.
1.22 They were shocked by his teaching because he taught them like someone with authority, not like the scholars. They were shocked by his teaching because he taught them like someone with authority, not like the scholars. They were shocked by his teaching because he taught them like someone with authority, not like the scholars. They were shocked by his teaching because he taught them like someone with authority, not like the scholars.
1.23 Then there was a man with an unclean spirit in their synagogue. He screamed, Then there was a man with an unclean spirit in their synagogue. He screamed, Then there was a man with an unclean spirit in their synagogue. He screamed, Then there was a man with an unclean spirit in their synagogue. He screamed,
1.24 "What's with us and you, Jesus Nazarene? Have you come to destroy us? I know who you are — God's holy one!" "What's with us and you, Jesus Nazarene? Have you come to destroy us? I know who you are — God's holy one!" "What's with us and you, Jesus Nazarene? Have you come to destroy us? I know who you are — God's holy one!" "What's with us and you, Jesus Nazarene? Have you come to destroy us? I know who you are — God's holy one!"
1.25 Jesus told it off saying, "Be quiet! Get out of him!" Jesus told it off saying, "Be quiet! Get out of him!" Jesus told it off saying, "Be quiet! Get out of him!" Jesus told it off saying, "Be quiet! Get out of him!"
1.26 Throwing a fit and shouting with a loud voice, the unclean spirit got out of him. Throwing a fit and shouting with a loud voice, the unclean spirit got out of him. Throwing a fit and shouting with a loud voice, the unclean spirit got out of him. Throwing a fit and shouting with a loud voice, the unclean spirit got out of him.
1.27 All being shocked they asked each other, "What is this? What new teaching is this, that with authority he gives orders even to unclean spirits and they obey him?" All being shocked they asked each other, "What is this new teaching with authority? He gives orders even to unclean spirits and they obey him." All being shocked they asked each other, "What is this, this new teaching with authority? He gives orders even to unclean spirits and they obey him." All being shocked they asked each other, "What is that teaching, this new one with authority, that he gives orders even to unclean spirits and they obey him?"
1.28 The news about him then got out everywhere in the whole region of Galilee. The news about him then got out everywhere in the whole region of Galilee. The news about him then got out everywhere in the whole region of Galilee. The news about him then got out everywhere in the whole region of Galilee.
1.29 Then, leaving the synagogue, they went to Simon and Andrew's house with Jacob and John. Then, leaving the synagogue, they went to Simon and Andrew's house with Jacob and John. Then, leaving the synagogue, he went to Simon and Andrew's house with Jacob and John. Leaving the synagogue, he went to Simon and Andrew's house with Jacob and John.
1.30 Simon's mother-in-law lay sick with fever. Then they tell him about her. Simon's mother-in-law lay sick with fever. Then they tell him about her. Simon's mother-in-law lay sick with fever. Then they tell him about her. Simon's mother-in-law lay sick with fever. Then they tell him about her.
1.31 He went over, took hold of her hand, and helped her up. The fever left her and she began to wait on them. He went over, took hold of her hand, and helped her up. The fever left her and she began to wait on them. He went over, took hold of her hand, and helped her up. The fever left her and she began to wait on them. He went over, took hold of her hand, and helped her up. The fever left her and she began to wait on them.
1.32 In the evening after sunset they began to bring everyone who was suffering from sickness and the demonized. In the evening after sunset they began to bring everyone who was suffering from sickness and the demonized. In the evening after sunset they began to bring everyone who was suffering from sickness and the demonized. In the evening after sunset they began to bring everyone who was suffering from sickness and the demonized.
1.33 The whole town was gathered at the door. The whole town was gathered at the door. The whole town was gathered at the door. The whole town was gathered at the door.
1.34 He cured a lot who suffered a variety of sicknesses and got out a lot of demons. He did not allow the demons to speak because they had recognized him. He cured a lot who suffered a variety of sicknesses and got out a lot of demons. He did not allow the demons to speak because they had recognized him to be Christ. He cured a lot who suffered a variety of sicknesses and got out a lot of demons. He did not allow the demons to speak because they had recognized him to be Christ. He cured a lot who suffered a variety of sicknesses and got out a lot of demons. He did not allow the demons to speak because they had recognized him.
1.35 Getting up early while it was still dark, he left and went away to a deserted spot and prayed there. Getting up early while it was still dark, he left and went away to a deserted spot and prayed there. Getting up early while it was still dark, he left and went away to a deserted spot and prayed there. Getting up early while it was still dark, he left and went away to a deserted spot and prayed there.
1.36 Simon and those with him hunted him down. Simon and those with him hunted him down. Simon and those with him hunted him down. Simon and those with him hunted him down.
1.37 They find him and say to him, "Everyone is looking for you." They find him and say to him, "Everyone is looking for you." They find him and say to him, "Everyone is looking for you." They find him and say to him, "Everyone is looking for you."
1.38 He says to them, "Let's go somewhere else -- into the next towns -- so that I can campaign there too, because I came out for this." He says to them, "Let's go somewhere else -- into the next towns -- so that I can campaign there too, because I came out for this." He says to them, "Let's go somewhere else -- into the next towns -- so that I can campaign there too, because I came out for this." He says to them, "Let's go somewhere else -- into the next towns -- so that I can campaign there too, because I came out for this."
1.39 He was campaigning in their synagogues throughout Galilee, driving out demons too. He went campaigning in their synagogues throughout Galilee, driving out demons too. He was campaigning in their synagogues throughout Galilee, driving out demons too. He was campaigning in their synagogues throughout Galilee, driving out demons too.
1.40 A leper came towards him begging and kneeling to him, saying "If you want to you can make me clean." A leper came towards him begging and kneeling, saying "If you want to you can make me clean." A leper came towards him begging and kneeling, saying "If you want to you can make me clean." A leper came towards him begging, saying "If you want to you can make me clean."
1.41 Deeply moved, reaching out his hand he takes hold of him and says: "I want to. Be clean." Deeply moved, reaching out his hand he takes hold of him and says: "I want to. Be clean." Deeply moved, reaching out his hand he takes hold of him and says: "I want to. Be clean." Getting annoyed, reaching out his hand he takes hold of him and says: "I want to. Be clean."
1.42 Then the leprosy left him and he was cleansed. Then the leprosy left him and he was cleansed. Then the leprosy left him and he was cleansed. Then the leprosy left him and he was cleansed.
1.43 He told him off then sent him away. He told him off then sent him away. He told him off then sent him away. He told him off then sent him away.
1.44 He says to him, "Look, don't say anything to anyone. Instead, go off, show yourself to the priest, and offer what Moses commanded for your cleansing as proof to them." He says to him, "Look, don't say anything to anyone. Instead, go off, show yourself to the priest, and offer what Moses commanded for your cleansing as proof to them." He says to him, "Look, don't say anything to anyone. Instead, go off, show yourself to the priest, and offer what Moses commanded for your cleansing as proof to them." He says to him, "Look, don't say anything to anyone. Instead, go off, show yourself to the priest, and offer what Moses commanded for your cleansing as proof to them."
1.45 However, he went out and began much campaigning and spreading the word so that Jesus couldn't openly go into a city anymore but stayed outside in remote places. They came to him from everywhere. However, he went out and began much campaigning and spreading the word so that Jesus couldn't openly go into a city anymore but stayed outside in remote places. They came to him from everywhere. However, he went out and began much campaigning and spreading the word so that Jesus couldn't openly go into a city anymore but stayed outside in remote places. They came to him from everywhere. However, he went out and began much campaigning and spreading the word so that Jesus couldn't openly go into a city anymore but stayed outside in remote places. They came to him from everywhere.

Notes

  1. My translation attempts to produce contemporary English while retaining the atmosphere of the Greek. I've used "change your attitude" instead of the somewhat archaic "repent," and "campaign" instead of the rarely used "proclaim" or less vivid "preach." The simple present is used to translate Mark's "historic present." (E.g. "He says to them...")

  2. Sometimes the most frequently supported readings of the four varieties are all the same, as in Mark 1.6 where two witnesses from cluster D have leather instead of hair.

  3. A variation unit may affect more than one verse, as at Mark 1.7-8.

Finally, a plea. Please share information in a format that is useful to others. Given the volume of data and human cost of manual transcription, sharing data as electronic files is a Good Thing. (CSV is a good choice; so is XML.) Please present data sets as data matrices or something like the following XML that can be processed to produce a data matrix using a language such as XQuery:


      <apparatus>
        <site id="Mk.1.1.1">
          <reading code="1">
            <text>Χριστου υιου θεου</text>
            <attestation>UBS Aleph-1 B D L W 2427</attestation>
          </reading>
          <reading code="2">
            <text>Χριστου υιου του θεου</text>
            <attestation>A Delta f-1 f-13 33 180 205 565 579 597 700 892 1006 1010 1071 1243 1292 1342 1424 1505 Byz E F G-supp H Sigma Lect eth geo-2 slav</attestation>
          </reading>
          <reading code="3">
            <text>Χριστου υιου του κυριου</text>
            <attestation>1241</attestation>
          </reading>
          <reading code="4">
            <text>Χριστου</text>
            <attestation>Aleph Theta 28-c syr-pal arm geo-1 Origen Asterius Serapion Cyril-Jerusalem Severian Hesychius Victorinus-Pettau</attestation>
          </reading>
          <reading code="5">
            <text>omit</text>
            <attestation>28 Epiphanius</attestation>
          </reading>
          <reading code="NA">
            <text>undefined</text>
            <attestation>it-a it-aur it-b it-c it-d it-f it-ff-2 it-l it-q it-r-1 vg syr-p syr-h cop-sa cop-bo Irenaeus Ambrose Chromatius Jerome Augustine Faustus-Milevis</attestation>
          </reading>
        </site>
        <site id="Mk.1.2.1">...</site>
        ...
        <site id="Mk.16.20.1">...</site>
      </apparatus>
    

Having information that can be electronically processed to produce data matrices allows a broad range of analysis techniques to be applied. If unable to release data matrices then distance matrices are useful, though the range of applicable analysis techniques is narrower. (Tables of percentage agreement or proportional agreement are readily converted to distance matrices.) An important adjunct to distance matrices (or tables of agreements) is a table of counts saying how many data points were used to calculate each distance. Not supplying this information leaves others in the dark concerning the statistical significance of the distances.

Another important aspect is to present apparatus data in a manner that allows presence or absence of all witnesses chosen for citation to be readily established. (See, for instance, the category with text "undefined" in the above XML example.) The UBS Greek New Testament apparatus is most useful in this respect: one knows that if a sometimes cited witness does not appear in an apparatus entry then its reading cannot be established at that place. The Nestle-Aland Novum Testamentum Graece apparatus is less easy to use for constructing data matrices because there are a number of reasons why a witness might not be cited at a variation site:[29]

  • it is subsumed under the majority text (𝔐)

  • it does not support the noted reading of a negative apparatus entry

  • its text is not legible.

In the absence of an algorithm to establish why a witness is not cited at a variation site, this kind of apparatus is not amenable to producing data matrices.

Richard Mallett deserves special thanks for encoding data matrices and transcribing tables of percentage agreement from numerous sources. Compiling the basic data from which analysis proceeds is an arduous and painstaking task, and he has done great service in this respect. Mark Spitsbergen also deserves thanks for helping to encode UBS4 apparatus data for the first fourteen chapters of Matthew.

Maurice A. Robinson kindly provided tables of percentage agreement for the Gospels and Acts. These are derived from the apparatus of the second edition of the United Bible Societies' Greek New Testament. The exacting task of transforming the data into electronic format was performed by Claire Hilliard and Kay Smith.

A number of the results are produced from comprehensive data generously provided by the Institut für neutestamentliche Textforschung in Münster, Germany. Researchers at the INTF have spent many years on the gargantuan task of compiling this data. Holger Strutwolf, Klaus Wachtel, and Volker Krüger were instrumental in providing access to the data.

Thanks go to Gerald Donker for suggesting that the RGL plotting library be used to produce three-dimensional CMDS maps. He also encouraged me to take a less procrustean approach to missing data. As a consequence, the analysis results presented here include many more witnesses than they otherwise would.

The analysis would scarcely have been possible without the marvellous R Language and Environment for Statistical Computing.

Isaac Newton said, If I have seen further it is only by standing on the shoulders of giants. This sentiment truly applies to the results presented here. Our field owes a great debt to those who have compiled the information, both printed and electronic, upon which the data and distance matrices are based.

A. Supplementary Information

This appendix provides supplementary information related to analysis results for the data sets:

Table A.1. Supplementary information

Section Source Proportion (CMDS) MSW plot (PAM)
Matthew Brooks 0.54
CB 0.73
Cosaert 0.60
Ehrman 0.63
INTF-Parallel 0.28
Racine 0.69
UBS2 0.35
UBS4 0.53
Wasserman 0.51
Mark CB 0.76
Cosaert 0.63
Hurtado (Mk 1) 0.76
Hurtado (Mk 2) 0.82
Hurtado (Mk 3) 0.76
Hurtado (Mk 4) 0.82
Hurtado (Mk 5) 0.76
Hurtado (Mk 6) 0.81
Hurtado (Mk 7) 0.85
Hurtado (Mk 8) 0.83
Hurtado (Mk 9) 0.79
Hurtado (Mk 10) 0.82
Hurtado (Mk 11) 0.77
Hurtado (Mk 12) 0.81
Hurtado (Mk 13) 0.82
Hurtado (Mk 14) 0.83
Hurtado (Mk 15+) 0.86
Hurtado (P45) 0.84
Mullen 0.62
Mullen (P45) 0.76
INTF-Parallel 0.33
UBS2 0.42
UBS4 0.51
UBS4 (control) 0.16
UBS4 (non-B) 0.45
UBS4 (non-Byz) 0.54
UBS4 (non-f-1) 0.47
UBS4 (non-it-ff-2) 0.46
UBS4 (non-vg) 0.51
Wasserman 0.59
Luke Brooks 0.45
CB 0.75
Cosaert 0.57
Fee (Lk 10) 0.67
INTF-Parallel 0.27
UBS2 0.38
Wasserman 0.61
John Brooks (C) 0.50
Brooks (it-j) 0.47
CB 0.72
Cosaert 0.45
Cunningham 0.54
EFH 0.61
Fee (Jn 1-8) 0.80
Fee (Jn 1-8, corr.) 0.77
Fee (Jn 4) 0.83
Fee (Jn 4, corr.) 0.83
Fee (Jn 4, pat.) 0.60
Fee (Jn 4, pat., corr.) 0.60
Fee (Jn 9) 0.89
Fee (Jn 9, corr.) 0.87
INTF-Parallel 0.34
UBS2 0.36
Wasserman 0.57
PA Wasserman 0.66
Acts Donker 0.64
Donker (Acts 1-12) 0.66
Donker (Acts 13-28) 0.67
Osburn 0.76
UBS2 (Aleph) 0.40
UBS2 (P45) 0.36
General Letters Donker 0.83
James INTF-General 0.35
1 Peter INTF-General 0.35
UBS4 0.45
2 Peter INTF-General 0.36
1 John INTF-General 0.33
Richards 0.50
UBS4 0.40
2 John INTF-General 0.32
3 John INTF-General 0.35
Jude INTF-General 0.32
Paul's Letters Brooks 0.72
Cunningham 0.70
Donker 0.70
Osburn 0.78
Romans Donker 0.71
1 Corinthians Donker 0.68
2 Corinthians UBS4 0.44
2 Cor. - Titus Donker 0.66
Hebrews Donker 0.72
UBS4 0.39
UBS4 (B) 0.41
Revelation UBS4 0.41



[1] Defining the limits of a variation site is a matter of editorial discretion. See Potential Computer Applications for a discussion of some approaches.

[2] Gerd Mink provides a definition of the term initial text in Problems of a Highly Contaminated Tradition, 25-26. Eldon J. Epp finds the term original text problematic, as discussed in his Multivalence of the Term 'Original Text.'

[3] My impression is that the original text's readings are likely to survive among those we know, and that applying the full range of tools now available (including conventional criteria for choosing the best readings) allows us to produce a good approximation to the original text at the level of individual words (though not their spelling). At the semantic level (which is what matters), comparing the archetypal texts (i.e. group representatives) shows that the New Testament is well established, with only a few places where there is a real question about the meaning of the text as handed down from the apostolic generation.

[4] See Analysis of Textual Variation for more details of the encoding conventions employed here.

[5] A distance matrix can be obtained from a table of percentage agreement by dividing each percentage by one hundred then subtracting the result from one. For example, a percentage agreement of 85% corresponds to a distance of 0.15.

[6] The vetting algorithm can be forced to retain a particular witness provided it has more than the minimum acceptable number of defined variation sites. The number fifteen is chosen because with this many variation sites a distance estimate of 0.5 has a sampling error of plus or minus 0.233. (In statistical terminology, the critical limits of the 95% confidence interval are 0.267 and 0.733.) That is, when using only fifteen variation sites, the sampling error associated with the distance estimate covers about one half of the entire range of possible distances. The relative size of the sampling error decreases as the number of sites from which the distance is calculated increases. A number less than fifteen may be used so that a particularly important fragmentary witness can be included in a distance matrix.

[7] The R script named control.r produces the control data matrix then dist.r is used to produce the corresponding distance matrix. Each variable in the control data matrix has only two possible states whereas more than two can occur in variables (i.e. variation sites) of the model data matrix. This is not a bad approximation as variables of the model data matrix often have only two states. The main aim, which is hardly affected by the number of states, is to produce a control with approximately the same mean distance between objects as the model. This is achieved using the R expression p = (1 + (1 - 2*d)^0.5)/2 to calculate the probability p of choosing the first state (i.e. 1) based on the desired mean distance d. This p is then used to set the chance of generating a 1 when c 1s and 2s are generated to form an object. Due to its stochastic nature, the procedure is unlikely to produce a control with exactly the same mean distance between objects as the model. However, if many controls were produced and their mean distances between objects were averaged then the result would tend towards d.

[8] The limits of the 95% confidence interval for the distance between two randomly generated objects can be obtained with the R expression qbinom(c(0.025, 0.975), c, d)/c where c is the number of variables and d is the mean distance.

[9] See Gerd Mink's Problems of a Highly Contaminated Tradition and Introductory Presentation for an explanation of the CBGM.

[10] Ranking is performed by rank.r.

[11] The bounds were calculated using the R expression qbinom(c(0.025, 0.975), 123, 0.464)/123. The mean distance between objects in the model distance matrix is 0.464 and the rounded mean number of variables in the objects from which that distance matrix was calculated is 123.

[12] CMDS analysis is performed by MVA-CMDS.r. The proportion of variance figure for each CMDS plot is provided in the "Supplementary Information" appendix.

[13] Eldon J. Epp, Significance of the Papyri, 291.

[14] In this article, a trajectory refers to a line joining two endpoints in textual space. By contrast, Epp uses the term to describe a time sequence of witnesses with the same kind of text; see e.g. his Twentieth-Century Interlude, 93.

[15] Maechler and others, Cluster Analysis Basics and Extensions; diana method of the cluster package.

[16] DC analysis is performed by MVA-DC.r using the distance matrix and a table of counts which gives the number of variation sites covered by each witness.

[17] Branching heights correspond to distances between the clusters constituted by the branches. The upper critical limit calculation and partitioning are performed by MVA-DC.r. The order of groups is determined by the program and is not significant.

[18] Naruya Saitou and Masatoshi Nei, The Neighbor-Joining Method, 406-7.

[19] NJ analysis is performed by pheno-NJ.r.

[20] See documentation relating to the pam method of the cluster package by Maechler and others, Cluster Analysis Basics and Extensions.

[21] PAM analysis is performed by MVA-PAM.r. There is no particular reason for making the largest number of groups twelve. Smaller numbers of groups tend to retain peripheral members while larger numbers tend to produce unwieldy tables of results.

[22] The MSW plot is produced by MVA-PAM-MSW.r. This script also identifies numbers of groups corresponding to peaks with above-average MSW values. An MSW plot for each data set is provided in the "Supplementary Information" appendix.

[23] Systematic bias can be introduced by editorial practices that treat one group differently to another.

[24] The script rank.r allows ranked distance results to be obtained for any witness included in a distance matrix. This script requires a distance matrix and list of counts of readings per witness in the distance matrix, which are found in the dist directory of this web site.

[25] Streeter (1924), 64.

[26] Frederik Wisse, Profile Method, 52-90.

[27] Aland and others, Greek New Testament (4th ed.), 2*.

[28] Minuscule 2427, which is now considered to be spurious, has been dropped from the B cluster so that it does not affect decisions on cluster membership.

[29] Aland and others, Novum Testamentum Graece (27th ed.), 51*.