Extracting data from textual communities

§8 Using Universal Resource Identifiers (URIs), scholars are able to retrieve the XML apparatus from the Textual Communities databases. Some instructions on how to do this can be found as part of the Textual Communities wiki, where there are a few examples of the naming structure for the URIs (Robinson 2020).

§9 Peter Robinson completed a new regularization of “The Wife of Bath’s Prologue” in 2019, and this is currently stored in the Textual Communities database. By using a positive collation URI, scholars can extract an XML apparatus, with an optimized alignment intended for use with phylogenetic software, as seen in Figure 1.

Figure 1

“The Wife of Bath Prologue” XML apparatus, extracted from Textual Communities. Regularized and aligned by Peter Robinson.

XML apparatus structure

§10 Each word (a token in CollateX language) is assigned an even number; thus, 2, 4, 6, etc., leaving the uneven numbers free for use with supplemental text not present in the base text for collation. The encoding of each place of variation is given as <app from="10" n="CTP2:entity=WBP:line=2" to="12" type="main">. In this particular case, "10" refers to what would be position five within the line. It could have said from="10" and to="10", which would have referred to a single word in the base text. Instead, from="10" and to="12" corresponds to positions 5 and 6 in the line. By contrast, the following word, “ynough” appears on its own: <app from="14" n="CTP2:entity=WBP:line=2" to="14" type="main">.

§11 Between the element attributes that determine the position in the line, there is another one, n="CTP2:entity=WBP:line=2", pointing out at the exact place in which this is occurring in the Wife of Bath’s Prologue. The attribute refers to line 2 of the entity “Wife of Bath’s Prologue,” which is part of the Canterbury Tales Project phase 2. The final attribute within the app entity is type="main". This indicates that the section in question is present as opposed to text not present (type="om") or to a physical gap in the document (type="lac"). The next line of code in Figure 1, shows the <lem> element with an attribute Base, the lemma against which all witnesses are compared and which has been tuned during the collation process to present the words in positions 10 and 12 together as a phrase. Thus, the lemma becomes “is right,” rather than being split into two lemmata, “is” and “right.” This alignment is an improvement both for the phylogenetic analysis and for the readability of the generated apparatus.

§12 The following element is a reading proper, <rdg>, which has the attribute n (with values a, b, c, etc.) and varSeq (with values 1, 2, 3, etc.). Both attributes are generated by Smith’s Collation Editor, the tool employed for both regularization and alignment. Immediately, this is followed by the attribute wit and with the sigils of the different witnesses as value. The content of the element <rdg> is, in this case, two words, the reference reading from the base text, “is right,” and the sigils of the individual witnesses marked up with the element <idno>. Each <idno> is followed by the other readings in this place of variation.

NEXUS file and variant matrix

§14 NEXUS files are commonly used in bioinformatics and are a standard format for phylogenetic software (Maddison, Swofford, and Maddison 1997). Each file has several sections, formally known as blocks. Every NEXUS file has, at least, a taxa block (a taxon is an organism recognized by researchers as a unit) and a data block. Our groupings refer to texts, not organisms, so each taxon is represented by a sigil corresponding to one of our witnesses. A typical NEXUS file (Bordalejo and Robinson 2020b) used by Canterbury Project researchers will have a block called statelabels, which records all the variants at every place of variation within that section of the text. Thus, for example:

3277 CTP2_entity_WBP_line_484_2_2 i and for_i 3278 CTP2_entity_WBP_line_484_4_12 made_hym_of_the_same 3279 CTP2_entity_WBP_line_484_14_14 wode hode clothe wede 3280 CTP2_entity_WBP_line_484_16_18 a_troce a_croce a_cote a_groce an_hode. (Bordalejo and Robinson 2020b)

Although the syntax is not designed for human reading, it is not difficult to understand. Each entry has an individual identifier in consecutive order, followed by the line indicators and the position within the line. The example above can be read as follows: these are characters 3277, 3278, 3279 and 3280 of the Wife of Bath’s Prologue’s line 484 locations 2 to 18 within phase 2 of the Canterbury Tales Project. The positions within line WBP484 are given in even numbers. This is part of the processing by Catherine Smith’s Collation Editor (Smith 2019). Because of the limitations of CollateX, which does not have regularization or alignment facilities, Textual Communities has also embedded a version of Smith’s Collation Editor. Catherine Smith, working for the Institute for Textual Scholarship and Electronic Editing at the University of Birmingham, developed the Collation Editor to regularize and align the Greek New Testament. For anyone familiar with the Editio Critica Maior (Aland and Strutwolf 1997), the use of only even numbers is understandable (see Figure 2).

Figure 2

The text and condensed apparatus of the Editio Critica Maior.

§15 The concept is that variants are given in reference to the text present in the base text using the even number system, but when text not present in the base text comes to light, this is labelled with odd numbers. Smith has followed the same architecture in her program, not only because her software is in use at the Institute for New Testament Research in Münster for the continued work on the Editio Critica Maior and the Nestle-Aland edition, but also because the idea is both sound and successful in practice.

§16 Returning to the specifics of the previous example (Figure 1), the places of variation are separated by a space in the nexus file, while phases are presented as a unit by using the underscore. This means that place of variation 2, the first in this line, has three variants:

• I

• And

• For I

While place of variation 14, the third in the line, has four variants:

• wode

• hode

• clothe

• wede

The second place of variation in the line is a phrase, “made hym of the same,” formed of places of variation 4 to 12. This means that it is comprised of the words which are in the second to the sixth positions in the line.

§17 For the Wife of Bath’s Prologue, there are 5463 places of variation after regularization and alignment.

§18 The following block of the NEXUS file (Figure 3) is the taxlabels block, where a number is assigned to each witness, starting with 0 for the Base text and going to 88, which corresponds to Wynkyn de Worde’s edition. The final section of a Canterbury Tales Project NEXUS is the variant matrix, just called Matrix in our file.

Figure 3

Detail of the matrix in the Wife of Bath’s Prologue NEXUS file.

§19 The matrix is difficult to read for humans, though not impossible. Each variant has been assigned a number. For example, in places of variation 16 and 18, which have been aligned together, there are four phrase variants:

• a troce

• a croce

• a cote

• a groce

• an hode

§20 Each of these variants gets assigned a number 0 (a troce), 1 (a croce), 2 (a cote), 3 (a grote), 4 (an hode). Text not present is marked with a question mark. Consider the following:

[3280] CTP2_entity_WBP_line_484_16_18 00?0??111011?1011?1??11?1111?1?11100?????11?1111??114?1111????11??0?111423?111111??11?1?0

§21 Witnesses that agree with the base text are represented as 0. By counting the position and reconciling that with the position of each witness sigil in the taxlabel block, one could figure out which witnesses agree with the base text, which with reading 1, 2, 3 or 4. Although not impossible to interpret, the task is time-consuming and not advisable. For human reading, it is much easier to present something more like a traditional print apparatus (whether positive or negative). The NEXUS file is needed to process it with Phylogenetic Analysis Using Parsimony and Other Methods (Swofford 2003. See Chaucer 2021), henceforth PAUP, or some other bioinformatics software. The NEXUS file for “The Wife of Bath Prologue” used in these examples was generated from the apparatus produced after Robinson’s regularization (Bordalejo and Robinson 2020a).

Phylogenetic analysis methods

§22 For textual scholars handling relatively large amounts of data, the use of computers might be quite obvious. And yet, only a small number of textual scholars make regular use of any kind of computer-assisted stemmatological method. Howe, Connolly, and Windram (2012) outline the reasons to employ these as part of one’s research:

These methodologies collectively offer several advantages to textual scholars. The use of computers means that the dataset can be sensitively yet comprehensively handled, which is particularly important if it comprises copies of a long text, for which a manual analysis may prove unwieldy. Multiple approaches can be applied to the same dataset for the purposes of comparison and testing. Perhaps most importantly for scholars of vernacular traditions, not all phylogenetic analyses are tied to the assumption that a single ancestor is responsible for each extant copy and some copies are capable in principle of showing multiple affiliations. (Howe, Connolly, and Windram 2012, 56)

§23 The ability to comprehensive handle data is one of the most significant aspects of the use of this software, because of the computer’s capacity to accurately record the enormous amounts of data derived from a single textual tradition (for a comparison between manual and computer-assisted methods, see Bordalejo and Vázquez 2021). And yet, the open possibility of continued testing of different approaches (including different models of phylogenetic inference) cannot be underestimated. Through the years, the Canterbury Tales Project researchers have done precisely this by employing multiple methods with the same dataset. Peter Robinson experimented with other software, such as SplitsTree (Huson and Bryant 2006), which I also tested for my work on the order of the Tales (Bordalejo 2003). PAUP (Swofford 2003) produced clearer results than SplitsTree and has become the standard tool for Canterbury Tales Project research. Although serious effort has gone into the development of software specializing in literary and historical textual transmission, neither RHM (Roos and Heikkila 2009) nor SemStem (Roos and Zou 2011) has yet been made widely available Tools to facilitate computer-assisted stemmatological analysis have been incorporated into Textual Communities, while Robinson continues conversations for the integration of stemmatological software into the system.

§24 The software aims to present a seamless interface to the users with the file conversion happening in the background in a similar way to which it currently converts the apparatus output into a NEXUS file. The plan is to integrate the software within Textual Communities to facilitate its use through a single interface and to produce results ready for display as part of digital editions produced using the Textual Communities API.

§25 To detail the debate over the validity of stemmatological methods in textual criticism would be beyond the purview of this paper, however, a brief account of the ongoing discussion around such methods demonstrates the impact/importance of our work. Although stemmatological methods have been used successfully to explore diverse textual traditions (Barbrook et al. 1998; Spencer, Bordalejo, Robinson, et al. 2003; Robinson 2003; Chaucer 2004; 2006; Eagleton and Spencer 2006; Windram et al. 2008; Phillips-Rodriguez 2010; Robinson 2012), the singular idea that the relationship between phylogenetic analysis and manuscript transmission was of analogy, rather of identity, prompted targeted essays focusing on the relationship between the two (Mooney et al. 2004). Independently of its justification, the use of tools originally developed for a specialized field and later implemented in another has not been without criticism (Robins 2007, Hanna 1996, Hanna 2000, Cartlidge 2001, Alexanderson 2018). For this reason, various experiments were carried out with artificial textual traditions (Spencer et al. 2004; Roos and Heikkila 2009) and, when these were still not considered enough, a response to the criticisms was published (Howe, Connolly, and Windram 2012) followed by further theoretical explanations (Bordalejo 2016). Those interested in the use of phylogenetic analysis or other stemmatological tools would acquire reasonable knowledge from the texts mentioned above.

§26 The success of phylogenetic methods with various textual traditions has been paralleled in other fields, most notably anthropology (Tehrani and Collard 2002; Tehrani and Collard 2009), folklore (Tehrani 2013; Tehrani, Nguyen, and Roos 2016), and archeology (Mendoza Straffon 2016). These applications, beyond molecular evolution, with its expansion to non-biological fields, gave rise to the concept of “phylomemetics” (Howe and Windram 2011).

§27 I have written about the theory and practice of using phylogenetic analysis in the research context of medieval manuscript traditions (Bordalejo 2003, 90–112). However, since the work remains unpublished, it seems pertinent to summarize some important matters in this piece. Phylogenetic software looks into the nucleotide sequences to isolate the three-letter words that encode individual amino acids and how the copying process sometimes results in the loss of a nucleotide that gets replaced by a different one giving rise to a mutation. A mutation, if it were to give rise to a successful advantage, would be inherited and become a feature (Bordalejo 2003, 91–92). The software can express its results as networks or trees. Stemmatologists might tend to favour tree-building software because its output appears closer to that of conventionally constructed stemmata. The type of data used and the tree-building method sort phylogenetic software into categories.

Data handling: Distance vs. discrete

§28 The data for use with phylogenetic software can be approached directly by structuring the data as a NEXUS file, as described above (this is what the Canterbury Tales Project does with textual data); or data can be converted into a distance matrix, as was done with the Canterbury Tales tale-order data (see below). When the only step for processing is the restructuring of data, one talks about a discrete method. When the data is processed and converted into a distance matrix, one talks about distance methods.

§29 Page and Holmes explain that “…[d]istance methods are based on the idea that if we knew the actual evolutionary distance between all members of a set of sequences, then we could easily reconstruct the evolutionary history of those sequences” (Page and Holmes 1998, 179). Unweighted pair-group method using arithmetic averages (UPGMA), Least Squares (LS), Minimum Evolution (ME), and Neighbor Joining (NJ) are all distance methods (Nei and Kumar 2000, 87–113). For each pair of taxa (or witnesses), the evolutionary distance, which is a measure of genetic diversity, is calculated. The constructed tree considers the relationships between the distance values (Nei and Kumar 2000, 87).

§30 Although it might seem obvious that by removing the conversion into a distance matrix, one might also remove another layer for the possible introduction of errors or the mediation of models that can impact the data, not all data are liable to simple restructuring. Such was the case of the tale-order data I analized as part of my NYU doctoral thesis (Bordalejo 2003). The tale-order work was based on my recoding of the charts by John Manly and Edith Rickert (Manly and Rickert 1940), but these data required conversion prior to analysis. Matthew Spencer, who was also part of the STEMMA Project, suggested the use distance methods and was the first to convert my tables so phylogenetic software could be tested with non-textual data. This serves as an example of the use of distance methods when direct data restructuring is not possible.

§31 Spencer used breakpoint distance method (BP) which worked because the witnesses shared a significant number of missing items and also because there were fewer common items missing between any given pair. Spencer’s algorithm generated upper and lower bound data which differ from each other in that “[t]he lower limit occurs when no common items were lost, and the upper limit is approached if there are many lost common items” (Spencer et al. unpublished). However, breakpoint distance “is only reliable when the number of transpositions is small” (Spencer, Bordalejo, Wang, et al. 2003, 102). At the time, Wang and Warnow had just devised Inverse of Expected BreakPoint Distance (INBP), which seemed better suited for the tale-order research (Wang 2001). Both methods are described in our article, “Analyzing the Order of Items in Manuscripts of The Canterbury Tales” (Spencer, Bordalejo, Wang, et al. 2003), where ME trees based on this data are presented. Fuller results of the tale-order analysis are presented in my NYU doctoral thesis which concludes that there is an undeniable coherence between tale-order and textual transmission in the tradition which suggests that the order was more often than not copied from an exemplar while, in few occasions, it was purposely altered by scribes or their supervisors (Bordalejo 2003. 190 and ff.). Because of the nature of the data, trees were built using ME and NJ, both phylogenetic inference methods that accept distance values as data input.

§32 Discrete methods, by contrast, use structured data without the extra step in processing. Thus, they are one step closer to the data than distance methods. Some data, like the tale-order data, because of their nature, must be encoded before processing. The NEXUS file based on the Wife of Bath’s apparatus is a restructuring of the data to be processed by phylogenetic software, but the data are not changed by such restructuring. Discrete methods “endeavour to avoid the loss of information that occurs when sequences are converted into distances” (Page and Holmes 1998, 187), which means that another degree of separation between the data and the resulting tree is avoided. Maximum Parsimony (MP) and Maximum Likelihood (ML) are discrete methods of phylogenetic inference. These methods differ on how they choose the trees they present:

The two major discrete methods are maximum parsimony and maximum likelihood. Maximum parsimony chooses the tree (or trees) that require the fewest evolutionary changes. Maximum likelihood chooses the tree (or trees) that of all trees is the one that is most likely to have produced the observed data (Page and Holmes 1998, 187).

§33 In my previous study, I explain that although these methods are particularly well-suited for dealing with textual variation, the fact that MP searches for the trees with the least number of changes is liable to present a simplified version of what might be a more complex tradition (Bordalejo 2003, 93). However, there is a more significant problem with MP. Nei and Kumar synthesize it as follows:

If there are no backwards and no parallel substitutions (no homoplasy) at each nucleotide site and the number of nucleotides examined (n) is very large, MP methods are expected to produce the correct (realized) tree (Nei and Kumar 2000).

§34 Homoplasy refers to both parallel and convergent evolution, both of which are cases of independent development of the same features. Textual scholars are familiar with this phenomenon during which different scribes in completely separate occasions introduce the same change to the text. Manly and Rickert call it agreement by coincidence. This type of inference does not work well with highly contaminated traditions. Fortunately, for 15th-century witnesses of the Canterbury Tales contamination a less significant issue that it is in larger traditions with a life-span of several centuries like that of the Mahābhārata or the Greek New Testament. Models of phylogenetic inference that are able to handle contamination and coincidental agreement would be advantageous for both large classical and medieval textual traditions.

The Canterbury Tales Project

§35 In 1999, The General Prologue on CD-ROM (Chaucer 2000) presented trees that were produced using SplitTrees and that were informative about an early split in the textual tradition from which Robinson hypothesized the alpha hyparchetype (a lost manuscript from which roughly half of the tradition descended).

§36 Despite this breakthrough, the control offered by PAUP was unparalleled, and its results were consistent with aspects of the textual tradition confirmed independently. Take, for example, the fundamental groups proposed by Manly and Rickert:

• Group a: Cn Dd En1 Ds Ma

• Group b: He Ne Cx1 Tc2

• Group c: Cp La Sl2

• Group d: En2 Ll1 Lc Mg Pw Mm Ph3 Ry2 Ld2 Dl Ha2 Sl1

• Independent pairs: Ad3 and Ha5, Bo1 and Ph2, En3 and Ad1, Mc and Ra1, Ps and Ha1, and Ra2 and Ht

§37 These groupings and most of the pairs are confirmed by analysis carried out by members of the Canterbury Tales Project (Chaucer 1996; Robinson 1997; Robinson 2003; Bordalejo 2003; Chaucer 2004). Phylogenetic software confirms part of Manly and Rickert’s manual analysis of the Tales. The phylogenetic trees offer enough new avenues of enquiry to open paths for further research. There are two main conclusions from our analyses that improve or correct Manly and Rickert:

1. The tales and sections did not circulate independently; witnesses share both for text and non-textual elements the same overall relationships.

2. Hg, El, Ch, (Ad3, Ha5), (Bo1, Ph2), (En3 Ad1), Mc and Ra1, Ps and Ha1, and Bo2 and Ht represent independent lines of descent from the archetype. We call them the o witnesses (Robinson 1997, 80).

§38 Neither of these two corrections to Manly and Rickert signal incompetency nor carelessness. They were both excellent editors and researchers. The hypothesis of the independent circulation of the Tales appears to have support from the fact that the work was left unfinished and some units shifted positions, but both the textual and non-textual data indicate that the Canterbury Tales circulated as a book rather than in booklets (Chaucer 1996; Bordalejo 2002; 2003; Chaucer 2004; 2006).

§39 The Canterbury Tales Project’s editions present Variant Maps, representations of the relations among the witnesses produced using phylogenetic software and displayed as unrooted tree-like graphs. Some scholars might consider this controversial, but I want to state it here as clearly as possible: the unrooted graphs, which we call Variant Maps in our editions, are stemmata. They are based on data informed by editorial judgement at every point and show genetic relations among textual witnesses. Although the stemmata could be rooted, a root is unnecessary because it would not change the relationships between the nodes (for more information on the fact that changing the root of a tree does not change the relationships between the witnesses see Robinson and O’Hara, 1993). Robinson and I deliberately choose not to create a visualization reminiscent of manual stemmata. It is not necessary as Robinson and O’Hara showed almost thirty years ago.

Why maximum parsimony

§40 Because MP does not require further data processing, it seems preferable to other approaches for use with textual variation. The underlying model of phylogenetic inference, seeking the most parsimonious tree, “…creates a tree that represents the smallest overall number of independent mutations…” (Howe, Connolly, and Windram 2012, 63). Elsewhere, I explain that these trees should not be expected to conform to the historical reality of a manuscript tradition, as they can only take into account the input data, which is generally textual rather than extra-textual (Bordalejo 2016, 568).

§41 As methods were tested, parsimony became our choice because ML required both more time and computer resources while not offering significantly better results. For a tradition with fewer witnesses, one can use MP, and PAUP will do an exhaustive search for all the possible trees before settling into the most parsimonious one. However, above a certain number of witnesses, it is better to start with a heuristic search:

A provisional MP tree is first constructed by using a procedure called step-wise addition algorithm, and this provisional MP tree is then subjected to some kind of branch swapping to find a more parsimonious tree (Nei and Kumar 2000).

§42 The Canterbury Tales Project routinely uses heuristic searches for the production of stemmata. These searches are complemented by comparing them with consensus trees when more than one equally parsimonious tree is found. When a section of a tree appears surprising, bootstrapping, a sampling technique that is repeated one hundred times to offer a percentage result in which higher numbers point towards higher reliability (for more details see Higgs and Attwood 2005, 169 and ff.), is used to clarify the witnesses’ support.

§43 The caveat for the use of MP is the problem of agreement by coincidence that, in large enough numbers, would produce an incorrect topology for the tree. One has to look elsewhere for a possible solution. T-REX is a webserver for the inference, validation, and visualization of phylogenetic trees developed by members of the Department of Computer Sciences at the Université du Québec à Montréal and which deals with the issue of lateral gene transfer (Boc, Philippe, and Makarenkov 2010; Boc, Diallo, and Makarenkov 2012). Testing T-REX with textual traditions opens the possibility of solving a significant problem within computer-assisted stemmatology. This solution would have at least as much impact as the application of Chi-Square for the detection of a change of exemplar (Windram, Howe, and Spencer 2005; Phillips-Rodriguez, Howe, and Windram 2009).

Using phylogenetics to explore the tradition

§44 After the apparatus data is formatted into a NEXUS file, it can be uploaded to PAUP (or another phylogenetic application accepting the same type of format). The principles outlined above are implemented, setting the software to seek MP trees using heuristic searches.

§45 For “The Wife of Bath’s Prologue,” where are two separate stemmata because the project’s research indicates that the Ellesmere manuscript (El) changes exemplars around line 400. This became clear during the research that led to the publication of The Wife of Bath’s Prologue on CD-ROM (Chaucer 1996; Robinson 1997, 79), and was independently confirmed by the chi-square analysis carried out by Windram, Howe, and Spencer, who point out the exact place of maximum chi-square value is character 3384, “which corresponds to line 404 in the text and is the location most likely to be the site of manuscript recombination” (Windram, Howe, and Spencer 2005, 194–95). This example shows very clearly the change of Ellesmere’s habitual position within the stemmata (clustering close to Hengwrt [Hg] and Christ Church [Ch]) as it does in Figure 5, to branching with Bo1, Ph2, Gg and Si (Figure 4). For most scholars, just the shift from one place to another is remarkable. However, Ellesmere grouping with those manuscripts is the same pattern of variation found in “The Squire’s Tale” (Bordalejo 2002, 200–3). Gg also has the tendency to shift positions in the stemmata, suggesting either a contaminated exemplar or multiple sources for the manuscript resulting in conflation. Both Gg and El share a relatively high number of this characteristic agreement below the archetype, an agreement that is both genetic and conflicts with the other textual characteristics these manuscripts present.

Figure 4

Stemma of lines 1 to 400 of “The Wife of Bath’s Prologue.”

Figure 5

Stemma of lines 401 to the end of “The Wife of Bath’s Prologue.”

§46 Manly and Rickert, for all their laboriously accurate work on the Canterbury Tales (Manly and Rickert 1940) were unable to understand major sections of the tradition, particularly archetypal variation and the internal relationships within the d group. The data is too vast for humans to classify, understand, and draw sound conclusions from. Computational methods can sort and classify accurately and allow scholars to concentrate on interpretation. Had Manly and Rickert had an automatic system to sort variants they would have been unlikely to have been confused by the witnesses carrying archetypal variation (Bordalejo and Robinson 2018). However, despite their correct identification of four manuscript groups (a, b, c, d) based on their manual analysis, Manly and Rickert were not able to make sense of archetypal variation retained in separate lines of descent, a series of witnesses which Robison has termed O, and which do not represent a genetic group.

VBase

§47 No matter how clear our understanding of how phylogenetic software works or what inference model underlies our results, it would be foolish to accept the resulting trees with blind confidence. For this reason, our editions include VBase, a variant database that allows us to perform complex variant distribution queries. These queries help us further analyze our stemmata and explain why the phylogenetic software rendered a given tree in a particular way.

§48 The Variant Map for line 65 of “The Miller’s Tale” shows an odd distribution in which Ellesmere agrees with the b group (Cx1, Ne, He, and Tc2) against both Hengwrt and Christ Church. Our experience is that these three manuscripts (El, Hg, and Ch) form a compact trio that often appears with other O witnesses and, since silk/grene are not readings that arise simply by mistake, further exploration is required (see Figure 6). VBase can retrieve answers to highly sophisticated questions, which might contribute to the assessment of whether there is more to this place of variation.

Figure 6

“The Miller’s Tale” Variant Map (Chaucer 2003).

§49 One might want to start with a relatively simple query that retrieves all the variants that support b as a genetic group. In order to establish which variants determine the b group, one must carry out a search, as illustrated in Figure 7. In our edition, some searches are preset (and additional information on them is offered) to facilitate the use of VBase for anyone without an in-depth knowledge of the textual tradition of the Canterbury Tales.

Figure 7

VBase preset search for b group variants (Chaucer 2003).

§50 There are three main steps for the retrieval of the b variants:

1. Eliminate archetypal readings, expressed in <14 of all and in two or more of Hengwrt, Ellesmere and Christ Church. That is, they should appear in fewer than two of the three or <2 of Hg El Ch (these witnesses share archetypal variation, and the b variants arose below the archetype).

2. Distinguish from the a group by excluding variants present in more than two of one of the a subgroups, expressed as <2 of En1 Ds1 Dd; and any variants in more than one of the other a subgroup (<2 of Ma Cn Dd). Notice that one witness, Cambridge CUL Dd 4.24, appears twice because it shares variants with both subgroups of a (b must have variants exclusive to itself to be considered a genetic group and these cannot be shared with another genetic group).

The separation of b is established by those variants that follow the above conditions while appearing in three b witnesses or in >2 of Cx1 He Tc2 Ne Ox1.

§51 The evidence indicates that b is a genetic group: there are 222 variants shared by the b witnesses not present in a significant number of the rest of the witnesses. VBase makes it possible to conduct all sorts of specialized queries. If one were curious as to the number of variants that Ellesmere shares with b against Hengwrt and the rest of the textual tradition, one could simply adjust the query, as seen in Figure 8. VBase returns 32 places of variation showing that Ellesmere, one of the most important manuscripts of the Tales and a manuscript that is generally in agreement with Hengwrt and Christ Church, preserves 32 instances of variation likely to have originated below the archetype and linking it to one of the most textually removed from the origin of the tradition. I will not argue here about the reasons for this, discussed elsewhere (Chaucer 2004; Stemmatic Commentary, MI65). This is merely an example of how VBase can be used to explore questions related to witness relationships.

Figure 8

VBase modified search to isolate variants shared by Ellesmere and the b group (Chaucer 2003).

§52 VBase is instrumental in helping us understand the variation on which PAUP has based every section of the tree. Manly and Rickert’s correct assessment of the fundamental witness groups shows that it is possible to observe and analyze data and make the correct deductions without the help of a computer. But further investigation, such as in my query trying to isolate the variants in which Ellesmere agrees with b, requires more precise tools.

§53 Using a combination of PAUP and VBase, we have been able to understand the O witnesses: not a genetic group, but witnesses representing independent lines of descent from the archetype. The O witnesses often preserve archetypal readings which are lost elsewhere in the tradition. These variants puzzled Manly and Rickert who were not able to correctly classify them. Again, this is not a criticism of their work, but rather evidence of the difficulties editors face when dealing with very large datasets.

§54 To search for O variants, one sets up a query in which the target is present in at least two of Hengwrt, Ellesmere and Christ Church and in fewer than 15 of all other witnesses. Technically, all archetypal variants preserved by any witness in the tradition should be O variants but, for our purposes, O variants are those that have been preserved (often on account of their difficulty) by a few scribes in a few witnesses derived in a more or less direct way from the archetype of the tradition (Chaucer 2004). These variants, because of their distribution within many lines of descent, and their nature, are likely to be Chaucerian. There are 37 such variants in the Miller’s Tale, as shown in Figure 9.

Figure 9

VBase preset search for O variants (Chaucer 2003).

§55 VBase allows researchers to carry out complex searches asking precise questions. Some of those questions might come from hypotheses put forward by other scholars or by one’s own observations of particular witnesses in the tradition. However, when PAUP presents unexpected groupings or places witnesses in surprising positions in the tree topology, VBase can help us understand what part of the data supports the visual representation and why.

Editorial