Balisage Paper: Visualizing textual collation

Overview

As explained above, an alignment table is a two-dimensional table that displays the full contents of all witnesses in intersecting rows and columns, where each cell contains either text or nothing. In Table I, above, each row contains all of the words from one witness to the textual tradition and the columns represent alignment points, that is, the columns align words that the editors regard as corresponding to one another across witnesses. If a witness has no reading at a particular location in the tradition the cell for that witness in that column is empty.

An alignment table, such as Table I above, avoids at least two of the limitations of a footnoted critical apparatus:

At the same time, an apparatus-based edition with a dynamic critical text, such as the continuous reading text above the apparatus in our example from the Greek New Testament (Figure 1), always reports explicitly the readings that the editor prefers in situations involving variation. That reporting is not automatic in an alignment table that records only the readings from the witnesses, since that sort of table lacks a dynamic record of the editor’s interpretation of moments of variation. For that reason, if an alignment table is to record an editor’s interpretation of variation, it must add that interpretation as a supplement to the transcriptions of the witnesses. This feature is discussed below.

An edition published as an alignment table, such as Ostrowski 2003, is sometimes called an interlinear collation or a Partitur (German for musical score) edition, where the synchronized presentation of the text of all witnesses in parallel rows resembles a conductor’s orchestral score, which represents the different instrumental parts in parallel rows and aligns the measures in columns according to where parts are sounded together. The first image below is the beginning of an autograph manuscript of Mozart’s Symphony No. 1 in E♭ Major (K. 16):^[20] The second is from the online edition of Ostrowski 2003.^[21]

Figure 2: Mozart Symphony No. 1 in E♭ Major (K. 16), autograph manuscript

Figure 3: Rus′ primary chronicle (interlinear collation)

Both of these visualizations use rows to represent parts (instruments for Mozart, manuscripts and editions for Ostrowski 2003) and columns to represent alignments.

Swapping rows and columns in alignment tables

In the discussion and examples above we describe rows as representing witnesses and columns as representing alignment points, but nothing in the concept of the alignment table prevents the editor from reversing that layout, so that each witness occupies a particular column and the alignment points are represented by the rows. If we swap the rows and columns of Table I, the result looks like the following:

These tables are informationally equivalent and each has advantages and disadvantages. In the case of digital editions of texts that are written in a left-to-right writing system, such as Darwin’s English-language On the origin of species, tension arises between the naturalness of placing each witness in its own row, to support continuous left-to-write reading (Table I), and the fact that after a fairly small number of words the display must either scroll horizontally (which users notoriously find less comfortable than vertical scrolling^[22]) or wrap blocks of text that consist of several lines.^[23] Arranging the witnesses in columns mitigates these limitations, but not without introducing its own complications:

Reducing repetition in alignment tables

An alignment table, whatever its orientation, involves a large (and often very large) amount of repetition. Unnecessary repetition during data entry creates opportunities for user error and unnecessary repetition in information modeling increases storage space.^[24] At the same time, repetition is not necessarily undesirable for communicating information, and the focus of this report is primarily on visualization, and not on modeling or processing. Users most naturally recognize pieces of information as related when they are physically close to one another,^[25] when they are similar in some way,^[26] and when they appear inside the same boundary or container.^[27] For these reasons, repeating words in each witness in an alignment table where they occur makes it easier in some ways for readers to perceive and understand the content of the individual witnesses.

It is possible in some circumstances to remove repetition in an alignment table by merging cells where adjacent witnesses contain the same readings. Table III, below, is informationally equivalent to Table II, above, but it removes variation by merging cells horizontally where witnesses share a reading.

An obvious limitation of this approach is that it is not possible to merge cells that are not adjacent to one another. In Table III all readings that are shared by witnesses happen to be shared by witnesses that are adjacent in the table (and adjacent chronologically, since the columns are arranged by date of publication), but On the origin of species also contains readings that are shared by witnesses that are not chronologically consecutive. There is no consistent ordering of the six editions in the tables above that would make all shared readings adjacent, and repeatedly changing the order of the columns to manipulate the adjacency would introduce unwanted cognitive friction by undermining the reader’s spatial memory.^[28]

Tokenization and alignment tables

The first stage of the Gothenburg Model, Tokenization, is where the witness texts are divided into units to be aligned. The default tokenization in the release versions of CollateX separates tokens at sequences of whitespace (that is, divides the text into orthographic words) and also breaks off boundary punctuation marks into their own tokens. Users can override this default. The three Darwin tables above use a custom tokenization rule that separates the text into words on whitespace but does not break off boundary punctuation into its own token, so that, for example, the text of all witnesses ends with the single token diversified., which includes a trailing dot, instead of with a sequence of the token diversified (without the dot) followed by the token . (just a dot).

Separating the input texts into words during tokenization does not require that the words be reported individually at the fifth and final Gothenburg stage, called Visualization^[29]. CollateX supports a process that it calls segmentation, which merges adjacent alignment points that share alignment properties. For example, all witnesses in our Darwin example have the same first token (The) and same third through fifth tokens (of the various,), but there are differences in the second token (result vs results) and the sixth (quite in four witnesses and nothing in the other two). With segmentation activated, Table I would look like:

The point of segmentation is that an open alignment point ends and a new one begins not with every new token, but only when the agreement pattern among witnesses changes. In this example the alignment-point columns alternate between those that show full agreement (columns 1, 3, 5, 7, 9, and 11) and those that show variation or indel situations (columns 2, 4, 6, 8, 10). It will not always be the case that columns will alternate in this way; for example, if there are two adjacent alignment points that both show variation, but with different patterns of agreement among witnesses, the two will be output consecutively.

An alignment table with segmentation can arrange the witnesses either in rows, as in Table IV, above, or in columns, as in Table V, below:

Regardless of the orientation of the table, it is also possible (with this example, but not universally) to combine the merged or shared readings with segmentation, as in:

As we said earlier, merging cells where witnesses share a reading is possible only with adjacent cells, which means that it is a useful visualization only where all shared readings are shared by consecutive witnesses. That pattern occurs in the example above, but it it not the case elsewhere in On the origin of species.

Single-column alignment table

A modification of the alignment table to deal with fact that shared readings can be merged visually only when the witnesses are adjacent in the table is the single-column alignment table. This visualization divides the output the same way as the segmentation examples, above—that is, it starts a new alignment point when the pattern of agreement among witnesses changes. As the name implies, though, instead of rendering different witnesses in their own columns and merging adjacent ones, it displays the readings for an alignment point in a list within a single column, e.g.:

The organization of the Readings column looks familiar because it is identical to a positive critical apparatus in that it groups and records the readings of all witnesses, and not only those that diverge from the editor’s preferred reading. In this case, for reasons discussed above, there is no dynamic critical text, and although we could select a copy text (such as the first edition as chronologically primary or the last as Darwin’s final, and therefore most experienced, expression of his ideas), there is no lost original to imagine and (re)construct. With that said, if we were to select one witness as primary, to be presented consecutively, the Readings column could be synchronized with it automatically and rendered as either a positive critical apparatus (as is) or a negative critical apparatus (by removing the sigla for the copy text and witnesses that agree with it from the apparatus entries).

We found the single-column alignment table useful during development because it provided the same information about an individual alignment point as we would find in a row of Table VI, except that the single-column alignment table could also record the agreement of witnesses that were not consecutive in chronological or any other consistent order, which is a feature that cannot be expressed in an alignment table. At the same time, although the single-column alignment table provides a useful representation of a single alignment point, it is difficult to read consecutively. All of the information needed to reconstruct the full, continuous text of any witness is present, but because of unpredictable layout and gaps in witnesses, the visual flow through a single witness is inconsistent, interrupted, and distracting. Insofar as the single-column alignment table is ultimately just a positive critical apparatus without a main text, it is not surprising that it reproduces the challenges of using a critical apparatus to read a single witness continuously, and it does so without the continuous and legible critical text that accompanies a traditional critical apparatus.

The best text in an alignment table

Unlike with critical apparatus layout, which foregrounds the editor’s assessment of the best reading by placing it—and only it—in the main text, the transcription and interlinear publication of all witnesses does not automatically include an editorial judgment about which reading to prefer at moments of variation. To incorporate editorial assessment, and not just transcription, into an interlinear collation editors can include, in parallel with the actual textual witnesses, their own determination of a best reading. In Figure 3 (from Ostrowski 2003), above, the black rows represent transcriptions from manuscript witnesses, the red row at the bottom represents the editor’s dynamic critical text, and the blue rows represent critical texts published by other editors. This arrangement makes it easy to see at a glance where the witnesses agree or disagree with one another, which readings the editor considers most authoritative at each location, and how other editors evaluated the same variation to arrive at their decisions about which readings should be incorporated into the critical text.

An interlinear edition overcomes many of the intellectual and cognitive limitations of a critical apparatus, but at the expense of being practical only with a fairly small number of witnesses because the difficulty of seeing the patterns of agreement grows as the number of witnesses in the edition increases. A related consideration, at least with respect to paper publication, is that an interlinear collation incorporates a large amount of repetition or redundancy, which increases the size (and therefore also the production cost) of the edition. For example, the paper edition of Ostrowski 2003, with approximately ten witnesses and editions, fills three volumes that contain a total of approximately 2800 8-1/2 x 11 pages and occupy approximately eleven inches of shelf space.^[30]

Redundant repetition is sometimes regarded instinctively as undesirable because by definition it contributes no information that is not already available in a different form. In the case of visualization, though, repetition that may be informationally redundant may nonetheless contribute to the rhetorical effectiveness of the edition. For that reason, repetition is not automatically a weakness that should be avoided in a visualization; it is, instead, a communicative resource with costs and benefits that must be assessed on their own terms.

Alignment table summary

Ordering challenges: Even when the number of witnesses is not large, an interlinear collation raises questions about how to order them. On the one hand, ordering the witnesses identically throughout the edition enables the reader to memorize their relative and absolute positions quickly, avoiding the cognitive friction that would arise from having to read the sigla carefully at every line to verify which readings go with which witnesses. On the other hand, it would be easier to see which witnesses share readings if those witnesses were adjacent to one another, and in that case the groupings (that is, the grouping-dependent orders) might vary at different locations in the edition. We find consistent order easier to understand, even when it means that not all shared readings will be rendered in adjacent or merged cells. In Ostrowski 2003, for example, the witnesses observe a consistent order and are grouped according to overall patterns of agreement suggested by a stemma codicum, even though that means that sometimes witnesses that share readings may be separated from one another visually by text from other witnesses.^[31]

Repetition challenges: An alignment table that does not merge witnesses, and that instead repeats readings for each witness in which they appear (such as Table I, above), makes it easy to read any individual witness continuously. At the same time, not merging adjacent cells where witnesses share a reading means that the reader has to determine at every alignment point which witnesses agree with which others. How easy that is depends on the visual similarity of the readings. For example, readings of different lengths may be recognized easily as different, while readings of the same length may require closer inspection and consideration.

Separating the recording of variation from its evaluation: Insofar as an alignment table contains an affirmative statement about what each witness says (or doesn’t say) at every alignment point, it avoids the selectivity that can prevent readers from forming their own assessments of an editor’s decision about whether two witnesses attest a significant difference. The continuous text above a critical apparatus necessarily presents a privileged reading, either as a dynamic critical text or as a best witness selected as a copy text. Because an alignment table presents a legible continuous view of every witness, it does not automatically have a single privileged text (whether a dynamic critical text or a best witness). The editor of an alignment table may incorporate a dynamic critical text by entering it alignment point by alignment point, in parallel with the witness data, as in Figure 3.

Comparing alignment tables and critical apparatus: Our (somewhat subjective) experience has been that:

Variation-unit (Colwell and Tune 1964)

The earliest discussion of the variant graph as both model and visualization of which we are aware is Colwell and Tune 1964, which appears not to have been cited subsequently in relevant literature until its rediscovery by Elisa Nury and Elena Spadini (Nury and Spadini 2020, p. 7), who reproduce the example below:

Figure 4: Variant graph (Colwell and Tune 1964, p. 254)

Colwell and Tune 1964 uses the term variation-unit to describe a location where not all witnesses agree.^[32] Their illustration records the text of the readings on what graph theory would call the edges, with no information recorded on the nodes. The discussion in their article leaves no doubt that they are also tracking, for each variation-unit, which readings are attested in which witnesses, although they do not include witness identifiers in their illustration.

Our term alignment point, discussed above, is not the same as the Colwell and Tune 1964 variation-unit because an alignment point includes both locations with variation and locations where all witnesses agree, while the variation-unit in Colwell and Tune 1964 refers only to locations where witnesses diverge. In Figure 4, then, there are three variation-units but six alignment points. The focus on locations with variation matters in Colwell and Tune 1964 because the authors propose that variation-units be counted to explore and assess relationships among witnesses, and most of their article focuses on principles for classifying and evaluating types of variant readings as part of the text-critical process.^[33]

Rhine Delta (Sperberg-McQueen 1989)

The next appearance of the variant graph that we have been able to locate is Sperberg-McQueen 1989, which is also mentioned in passing in Nury and Spadini 2020 (p 7, fn 19). Sperberg-McQueen 1989 does not include any images (the write-up originated as a two-page conference abstract), but it describes the confluence and divergence of readings as analogous to the branches of a river delta, adopting the label Rhine Delta for the model. The illustration below shows how the Rhine (and Meuse) split into multiple channels, some of which may then merge or continue to divide:

Figure 5: Rhine–Meuse delta^[34]

Annual average discharge of the Rhine and Meuse 2000-11

Under the term Rhine Delta, Sperberg-McQueen introduces many features and properties of the variant graph that serve as the focus of later work by others:

The Rhine Delta model as described in Sperberg-McQueen 1989 records textual readings and witness identifiers on nodes and witness identifiers (alone) on edges, which is also the way information is allocated among nodes and edges in CollateX.^[35] The following image is part of the CollateX variant-graph visualization of the data in Table I, but see also the excerpt from Documentation (CollateX) below, which explains how this visualization does not, in fact, expose all token information:

Figure 6: Variant graph (https://collatex.net)

Variant graph (Documentation (CollateX) version, with textual content on nodes)

As far as we can tell, Sperberg-McQueen 1989 appears not to have been discussed in any detail in the literature until the author republished the full text of the abstract himself on his own website after hearing a conference presentation that described a model with very similar properties. Sperberg-McQueen 1989 explains that:^[36]

Textgraph (Schmidt and Fiormonte 2006)

The abstract of the 2006 ADHO presentation by Schmidt and Fiormonte mentioned above was published as Schmidt and Fiormonte 2006, where the authors describe and illustrate a variant graph structure that they call a textgraph. The following image is from p. 194 of that conference abstract:

Figure 7: Textgraph (Schmidt and Fiormonte 2006, p. 194)

Textgraph is the term in Schmidt and Fiormonte 2006 for what is called a variant graph in Schmidt and Colomb 2009

The features of the textgraph in Schmidt and Fiormonte 2006 are largely the same as those of the variant graph in Schmidt and Colomb 2009, discussed immediately below.

Variant graph (Schmidt and Colomb 2009)

The first use we have been able to find of the term variant graph is in Schmidt and Colomb 2009, which presents the same general model as Schmidt and Fiormonte 2006, but in greater detail and with more explanation. The following variant graph image is from Schmidt and Colomb 2009, p. 510:

Figure 8: Variant graph (Schmidt and Colomb 2009, p. 510)

Variant graph (Schmidt and Colomb 2009 version, with textual content on edges)

Schmidt and Colomb 2009 emphasizes many of the same appealing features of the variant graph as a model as Sperberg-McQueen 1989: it reduces redundancy (see, for example, the extensive repetition in Table I), it permits the concise representation of textual editing operations (§3.4, pp. 503–04), and it supports specific computational operations on the graph itself (reading a single version, searching a multi-version text, comparing two versions, determining what is a variant of what, and creating and editing (§5, pp. 508–10)). The algorithm in Schmidt and Colomb 2009 for creating and editing a variant graph is progressive in the sense in which that term is traditionally used in multiple-sequence alignment, that is, it incorporates one singleton witness at a time into the graph.

The representation of the variant graph in Schmidt and Colomb 2009 puts both textual content and witness identifiers on the edges of the graph. The Start and End nodes, indicated by circled S and E, represent the starting and ending point of a traversal. There is exactly one path from start to end for each witness, which can be traversed by following the edges labeled for that witness. The dotted lines represent transposition edges; they function as references (the gray text is a copy of the black text with which it is connected by a tranposition edge) and are not part of any traversal.

The variant graph as a model in CollateX (Documentation (CollateX))

As mentioned above, the CollateX variant graph, similarly to the earlier Rhine Delta model and unlike the model in Schmidt and Colomb 2009, stores the tokens that contain textual readings on the nodes of the graph, and the only information that the Rhine Delta model and CollateX store on the edges is witness identifiers. Schmidt and Colomb 2009 do not mention this difference; the lone reference to Sperberg-McQueen 1989 in Schmidt and Colomb 2009 reads, in its entirety:

It is possible to transform either of the two representations (text on nodes vs text on edges) to the other automatically, which means that they can be implemented in ways that are informationally equivalent, but the difference nonetheless merits attention from a software-engineering perspective; see the discussion below.

CollateX was developed under the auspices of the EU Interedition research program in 2009, with Ronald Haentjens Dekker and Gregor Middell as the two project leads. (About (CollateX)) Middell brought Schmidt and Colomb 2009 to Haentjens Dekker’s attention (neither developer knew about Sperberg-McQueen 1989 at the time), they recognized the variant graph as a useful model of textual variation, and they modified what they found in Schmidt and Colomb 2009 to move the tokens off the edges and onto the nodes. Insofar as edges in graph theory express relationships between nodes, putting no information on the nodes and all information on the edges would reduce the nodes to nothing but meeting places for edges, with no information or properties of their own, which makes the meaning of the nodes opaque.^[37] But the developers of CollateX also had a more specific reason for putting the witness content on the nodes: the tokens that represent witness content in CollateX are complex objects with multiple properties, and not just string values. As Middell explains in Documentation (CollateX) (text in square brackets has been added):

Where transpositions occur (not in the example above), the CollateX variant graph represents them with dotted lines. This corresponds to a similar special type of edge between nodes that contain the same textual content in Schmidt and Colomb 2009, where it is represented by a dashed line (see the example at Figure 8). The use of a special type of edge avoids cycles when traversing the graph according to its principal edges.

The variant graph as a visualization in CollateX

The utility of the variant graph in modeling has been confirmed by the successful use of CollateX in projects, where the variant graph functions as the internal model of the result of the alignment process, which can then be rendered visually as a graph (as in Figure 6, above) or transformed into an alternative visualization format (such as the alignment table in Table I, above). The focus of the present report, however, is not on the variant graph as a model, but on its utility as a final-form graphic visualization that communicates alignment and variation information to end-users.

Any visualization of variation, including the textual (and pre-digital) critical apparatus, becomes difficult to read as the extent of the variation grows, and both the critical apparatus and the variant graph manage the volume of information with the help of methods that we can usefully compare to data compression. Both visualization and data compression are transformations of a data set that retain something that matters for a particular purpose while excluding details that don’t matter for that purpose. As the name implies, the purpose of data compression is creating an output file that is smaller (as measured in bytes) than the non-compressed input. The visualization of data typically (although not obligatorily) aims for a smaller presentation size. Smaller presentation in the case of a critical apparatus might entail including only variation that the editor considers significant and choosing a negative apparatus over a positive one. In the case of graphic visualization, the editor includes only selected information with the goal of fitting into a small space (such as a single screen or page) a representation of just what the editor considers important (for a particular purpose) about the data.

Data compression methods are commonly categorized as either lossless or lossy, where the difference is that the exact original data can be restored from lossless—but not from lossy—compression. A critical apparatus that includes only variation that the editor considers significant is lossy because it excludes—completely and irretrievably—information that end-users might consider important. Insofar as a critical edition is often used as a surrogate for original data sources, especially when original manuscript data is not easily available to users of the edition, variation that an editor regards as insignificant is not documented and not recoverable. Choosing a negative apparatus instead of a positive one, on the other hand, is lossless because a negative apparatus comes with an implicit default: the editor asserts that any witness not recorded explicitly in an apparatus entry agrees at that location in all significant features with the continuous reading text (whether dynamic critical text or copy text based on a favored manuscript).

Visualization in general is typically not intended to present (or enable the recovery) of all features of an original set of data. Researchers use visualization to tell a story (that is, in the case of textual collation, to communicate a theory of the text) by including some properties while omitting others, so that the properties needed for the communicative purpose will stand out clearly. For example, as the discussion of the CollateX variant graph above explains, a node in the CollateX variant graph contains not just plain textual characters (the string rendered inside an ellipse in the visualization), but a set of tokens, which are complex objects with multiple properties. The simplification in this variant-graph visualization is lossy because all properties except the normalized string value of the token are discarded and the string is represented once regardless of the number of aligned tokens with which it is associated, which makes the graph easier to read.

CollateX actually supports two variant-graph visualizations, a simplified one that silently merges tokens that share a normalized reading and a more information-rich one that performs the merge but also renders information about differences among original, non-normalized forms of the tokens. Consider the following collation input:

The witnesses all begin with the same two tokens (Look, ,) and end with the same token (!), but the tokens in the middle vary. Note especially that witnesses A and C contain the token koala (lower-case k), which an editor would align with Koala (upper-case K) in witness B. If we tell CollateX to normalize the tokens by ignoring case before performing alignment, the basic (simple) variant-graph visualization silently merges koala with Koala:

Figure 10: CollateX basic variant-graph output

With CollateX configured to ignore case differences during alignment, the basic variant-graph output silently merges lower-case koala with upper-case Koala

The rich variant-graph output from CollateX performs the same merge because we’ve told it that case differences should not be treated as significant during alignment. CollateX nonetheless retains its knowledge of those differences even while not letting them affect what it merges, and the rich variant-graph visualization exposes them:

Figure 11: CollateX rich variant-graph output (middle portion)

With CollateX configured to ignore case difference during alignment, the rich variant-graph output also merges lower-case koala (witnesses A, C) with upper-case Koala (witness B), but in this version the output exposes the original differences among the non-normalized tokens

The basic visualization is easier to read because less ink means less competition for the reader’s attention, and the main thing we see is where readings are considered the same for alignment purposes. As is often the case, easier to read entails a cost, which in this case is the suppression and concealment of differences that the editor considers insignificant for alignment purposes, but that might nonetheless be regarded as important in other contexts.

CollateX normalization is controlled by end-users, and although case-folding may be a very common type of normalization, it isn’t the only type. When working with a heavily inflected language, for example, a researcher might want to align on lemmatized forms, ignoring inflectional differences in order to focus on lexical differences by excluding grammatical ones. Birnbaum 2015 describes the implementation of a Soundex-like strategy for aligning texts in a heavily inflected language with unstable orthography, and Birnbaum and Eckhoff 2018 describes a strategy for aligning Greek and Old Church Slavonic texts by normalizing them to (only) part of speech, that is, aligning nouns with nouns, verbs with verbs, etc., as in the following example (reformatted from an image on p. 12):

Figure 12: Multilingual alignment with CollateX

Alignment of Greek and Old Church Slavonic by part of speech. Solid edge lines apply to both languages, dotted lines only to Greek, and dashed lines only to Old Church Slavonic

The developers in this case customized the variant graph output format in Figure 12 to render only the original, non-normalized string values of the tokens, but the tokens contained additional properties. In particular, they incorporated a part-of-speech property, which served as the shadow normalization used to perform the alignment, which the developers chose to exclude from the visualization because it was not important for understanding the texts.^[38]

An alignment table, which must align all tokens, neutralizes groupings that are present in a variant graph

A variant graph represents the alignment of corresponding tokens from different witnesses by placing the tokens on the same node. An alignment table represents that same type of alignment by placing the tokens in the same column. A single column in an alignment table, however, does not always correspond to a single node in a variant graph; it corresponds to what we called an alignment point above. That is, a column represents an alignment of all witnesses—including some with completely unrelated readings and some with no readings (represented by empty cells)—at a particular location in the alignment. In addition to putting readings from a shared variant-graph node in the same column, an alignment table puts readings from different variant-graph nodes in the same column when they have the same rank in the variant graph, where rank means that [e]ach node is assigned a higher rank than the highest ranked node that point[s] to it. (Wernick 2017; text in square brackets is added).^[39] For that reason, an alignment table is not able to distinguish readings from different witnesses that are in the same column because they were on the same node in the variant graph model from those that were on different nodes of the same rank.

Consider, for example, an alignment of The gray koala with The Gray koala (note the case difference). When Normalization is configured to ignore case differences, the middle word is stored on the same node in the variant graph:

Figure 13: Rich variant graph with case folding

The neutralization of case differences during the Normalization stage means that gray and Gray are on the same node

However, when case differences are not neutralized for alignment purposes the readings gray and Gray are stored on different nodes that have the same rank:

Figure 14: Rich variant graph without neutralization of case

If case differences are not neutralized during the Normalization stage gray and Gray are on different nodes, but the nodes have the same rank in the graph

The two different variant graphs above nonetheless correspond to the same alignment table, with gray and Gray aligned within the same column:

The relationship of gray to Gray is self-evident, and it’s easier to see from other examples why putting readings into the same column of an alignment table because their nodes have the same rank is a blunt tool. For example, the method would just as easily align The gray koala with The prehistoric koala or The toy koala (neither of which is a koala at all). What gray and Gray have in common is lexical identity, but that isn’t why they wind up in the same column. What they also have in common, and the only thing that gray and prehistoric and toy have in common, is that they happen to appear in the same context. This relationship is what we called a non-match above, it is a meaningful part of the alignment result, and the alignment table renders it correctly. Our point is that in doing so, the alignment table must neutralize a distinction that is not neutralized in a variant graph.

If we think of the columns of an alignment table as representing the alignment points that we defined earlier, an alignment table does a poor job of representing groups of readings within an alignment point. Readings that share a node (by virtue of evaluating as equal according to a comparison of their normalized properties) can be considered to form a group, while those that share a rank but not a node may wind up at the same rank only because of properties of their neighbors. As we noted in our discussion of alignment tables, above, rows with readings that share a node are not necessarily even adjacent to one another; an alignment table is not designed to represent groupings within a column. This means that expressing a variant-graph model as an alignment-table visualization entails a loss of grouping information.

The fact that CollateX assigns witness readings to columns according to node rank in the variant graph model creates a challenge when there are gaps in a witness. Consider the following witness data:

The first (one) and last (two) tokens of the witnesses match, but the remaining middle token in Witness A, gray, does not match any of the three middle tokens in Witness B exactly (note that gray in Witness A is spelled differently than grey in Witness B). The variant graph output of CollateX for this alignment is:

Figure 15: Variant graph without an exact match

The variant graph shows the different paths for Witnesses A and B without aligning all tokens and without representing the absence of readings explicitly

Editors might disagree about how best to transform this variant graph into an alignment table visualization, but it is unlikely that any editor will prefer the default alignment table created by CollateX:

The reason CollateX defaults to an alignment table layout that most editors would consider suboptimal is that the CollateX default settings have no understanding of closest match. This reflects an engineering decision: finding a closest match is both unnecessary in most cases and much more expensive computationally than finding an exact match, which means that it would not be realistic to make it the universal behavior during the Alignment stage of the Gothenburg Model. Correspondences that can be described with simple algorithms, such as case differences, can be neutralized easily during the Normalization stage, at which point CollateX can look for exact matches across normalized values without additional computational cost. In principle users could create a Normalization function that would neutralize the distinction between gray and grey, but because the difference is lexically idiosyncractic (e.g., we would not want to normalize pray and prey the same way), implementing this sort of function would require considerably more developer time and effort than implementing case-folding.

Users who agree in their dislike of Table IX might disagree about what they would prefer to see in its stead. One option would be to align gray with grey, as in:

Users can ask CollateX to produce the alignment table above (Table X) by switching on the near matching option, which aligns tokens according to the closest match only when two conditions are satisfied: 1) there is no exact match and 2) there is a gap in the shorter witness.^[40] Near matching creates a non-traversal near-matching edge in the variant graph, which is used to adjust the rank of the nodes, which in this case produces:

Figure 16: Variant graph with near matching

Turning on near matching creates a non-traversal near-matching edge that is interpreted as overriding the default ranking algorithm to assign the same (higher) rank to nodes connected by a near-matching edge.

Assigning the same rank to nodes causes their readings to be rendered in the same column in a corresponding alignment table because assignment of a node to a column in CollateX is controlled by rank. What makes near matching tractable in this situation is that it is invoked during the Analysis (not Alignment) stage of the Gothenburg Model and only when the two conditions mentioned above are met. That situation arises rarely enough that the limited use of near matching does not impinge noticeably on the overall performance.

A different user option might be to align gray with the complex phrase white and gray, as in:

The user can invoke this behavior by switching on segmentation, which collapses adjacent nodes along a path in the variant graph as long as the edge labels (the witness identifiers) do not change. Turning on segmentation creates a different variant graph, one with fewer nodes, and the node with gray in Witness A winds up in the same column as white and grey in Witness B because the nodes have the same rank in the graph:

Figure 17: Variant graph with segmentation

With segmentation switched on, the tokens white and grey in Witness B are assigned to the same node in the graph, which maps to Table XI as an alignment table because the nodes in the middle have the same rank

Reasonable persons might disagree about whether the version with segmentation above (Table XI) offers a better alignment than the one with near matching (Table X). Ultimately, we would not expect automated collation to identify a single best alignment in situations where human editors might prefer different alignments for different reasons. CollateX accommodates different user preferences by incorporating switches that put these decisions under user control.^[41]

We’ve devoted a lot of attention above to complexities involved in expressing a variant graph model as an alignment table because the underlying issue is that there cannot be an unambiguous, round-trippable relationship between a variant graph and an alignment table because a variant-graph visualization tells a different story about alignment than an alignment table.^[42] The variant graph in Figure 15 does not strictly align gray with any one of the three tokens in the other witness because the exact position of a node on a variant-graph path is not informational (as long as the order of the nodes does not contradict the order of the text in the input documents). What the variant graph model does record is that the path from The to koala for Witness A passes through a single node and the one for Witness B passes through three nodes (in a specific order). The CollateX visualization of a variant graph has to plot the gray node of Witness A somewhere, and by default it does that according to the rank of the node in the graph, but the location of a node along a path is a rendering artifact that is not part of the information value of the variant-graph model. This differs from the meaning of a column in an alignment table; when the CollateX alignment table assigns nodes with the same rank to the same column, that decision does acquire an informational meaning because the meaning of a column in an alignment table is that all cells in the column correspond to one another for alignment purposes. The correspondence may happen because the tokens are evaluated as having the same value (after the Normalization stage) for alignment purposes (in which case they are on the same node) or it may happen because they are on different nodes that have the same rank (whether originally or because of near matching). In some cases correspondence because of shared rank may reflect a complete non-match, where non-matching tokens wind up sandwiched between matching ones, as in The gray koala vs The other koala; we refer to this as a forced match because the readings are forced into the same column by their neighbors, who leave them nowhere else to go.

The upshot of this difference is that tokens that share a column in an alignment table are presented as aligned, and because every token must appear in exactly one column and every witness must be represented by either a token or a gap in every column, an alignment table asserts an explicit alignment for every token or gap in each witness with either a token or a gap in every other witness. A variant graph, on the other hand, represents readings as associated only when they share a node and, differently, when they are connected by a near-matching edge. In other situations a variant graph makes no explicit assertion about the relationship of readings in one witness to readings in other witnesses. This means that a variant-graph visualization, but not an alignment table, can represent the difference between readings that share a node and readings on different nodes that share a rank.

A variant graph (but not an alignment table) can represent transposition and near matching

The term transposition describes a situation where witnesses cannot align all matching nodes simultaneously because corresponding nodes in different witnesses are ordered differently. Consider:

These witnesses have exactly the same tokens, but because an alignment cannot rearrange the order of tokens within a witness, CollateX cannot align both white with white and gray with gray. The current CollateX Java release (although not the Python one) is able to render information about transposition in the variant-graph visualization:

Figure 18: Variant graph with transpositions

The dashed lines represent non-traversal transposition edges, which connect nodes that are transposed with respect to each other

If we ask CollateX to create an alignment table from this variant graph it outputs:

An alignment table cannot align both gray and white without reordering witness content, which is not permitted, and in this case chooses to align and, which meant that it could not also align either gray or white. If we remove the conjunction from the witnesses, though, CollateX will align one of the color terms and not the other:

CollateX could have chosen to align white instead of gray, but it cannot align both.

As we’ve seen above (e.g., in Figure 16), a variant-graph visualization is able to represent near matching with a non-traversal near-matching edge. As we’ve already discussed, though, an alignment table is not able to distinguish a situation where readings wind up in the same column because they share a node from one where they wind up in the same column because near matching assigns the same rank to them. What transposition and near matching have in common is that graphs allow typed edges, which makes it possible to incorporate non-traversal transposition and near-matching edges into a variant graph visualization alongside the regular traversal edges. A table has only cell content, row membership, and column membership to work with, and therefore is not able to represent these different types of relationships except by layering non-table-cell properties such as color or arrows or footnotes on top of the inherent properties of content, row membership, and column membership.

Stemmaweb

The Stemmaweb project (Stemmaweb) offers a suite of online tools for exploring textual transmission. The term stemma in textual criticism refers to a hierarchical model of textual transmission, largely comparable to a phylogenetic tree in biological classification.^[43] A stemma represents an editor’s hypothesis about the historical transmission of textual information, and Stemmaweb accepts a collation as input into phylogenetic algorithms that create stemmatic representations of the textual tradition. Stemmaweb also incorporates a tool called the Stexaminer, which accepts as input a moment of variation in the collation, determines whether that variation is compatible with a particular stemma, and identifies, in cases involving mismatches with the stemma, where the change responsible for the nonstemmatic variation may have been introduced into the tradition. A mismatch may reflect a suboptimal stemma or a real textual phenomenon, such as contamination or coincidence.^[44]

The Relationship mapper component of Stemmaweb renders a variant graph that the user can then edit. As the documentation for the tool explains:

The Relationship mapper allows the user to create new edges in the variant graph that specify the type of a relationship between nodes. In the image below, copied from the documentation at Relationship mapper (Stemmaweb), the user has added an edge of type grammatical to record that two nodes store tokens with readings that are grammatical variants of each other:

Figure 19: Adding edges in the Relationship editor (Stemmaweb)

The human editor has created an edge of type grammatical between two nodes

The Relationship mapper also makes it possible to correct what the editor regards as mistakes in the collation. The following images, copied from Relationship mapper (Stemmaweb), show an incorrect collation and the corrected version after the editor has intervened manually within the Relationship mapper:

Figure 20: Incorrect collation (before manual intervention in the Relationship mapper)

CollateX aligned the wrong instances of et, which resulted in failing to align cetera, secundo, and asserendo

Figure 21: Corrected collation (after manual intervention in the Relationship mapper)

The editor has manually realigned et and then accepted suggestions that the Relationship manager generated, in response to that change, about realigning other tokens

Changes introduced into the variant graph through the Relationship mapper interface become accessible to Stemweb, the Stemmaweb component that generates a stemma from the collation. This means that a user who has edited the variant graph can rerun the Stemweb tool, specifying which types of relationship edges are likely to be stemmatically significant, and therefore taken into consideration when computing a new stemma, and which types should be excluded from the stemmatic analysis.^[45]

The variant graph provides a natural interface for this type of manual intervention because it contains relevant information that cannot be represented in an alignment table. As we described above, readings may wind up in the same column of an alignment table for a variety of reasons, which means that the presence of two readings in the same column does not distinguish whether they are there because they share a node or because they are on different nodes of the same rank. More generally, an alignment table is not designed to accommodate any representation comparable to the typed edges that can be added manually in the Relationship mapper. One way to describe the difference between the alignment table and the variant graph for editorial purposes is that the alignment table may tell a more legible story about which readings are aligned, but only the variant graph, especially when enhanced with the typed edges of the Stemmaweb Relationship mapper, is able to tell a story about why particular readings are aligned. Because the editor’s responsibilities include assessing and evaluating—and not merely recording—textual correspondences, the variant graph, enhanced with typed edges, offers a more effective interface than the alignment table for manual fine-tuning in support of stemmatic analysis.

A critical apparatus that records significant variation can also identify the type of variation in a way that is similar to the typed grammatical-variant edge introduced manually through the Stemmaweb Relationship editor in Figure 19. See, for example, the parentheses around the spelling variant identified at the end of Figure 1, where the preface to the edition explains that [p]arenthesis marks in the apparatus indicate that a witness or edition supports the reading for which it is cited, but with minor differences. (Greek New Testament, p. xlvii) There are nonetheless at least two reasons to prefer editing a variant graph over editing a critical apparatus for stemmatic exploration:

Standalone Collation Editor

The interface for modifying collation information in the Standalone Collation Editor presents a critical apparatus that summarizes the readings at all alignment points.^[46] The image below shows several alignment points in a sample Greek text distributed with the Standalone Collation Editor. In this image the user has hovered over one of the readings, which causes a tooltip overlay to appear that lists the sigla of witnesses that attest that particular variant:

Figure 22: Standalone Collation Editor (showing witness sigla)

The user has hovered over the first reading, causing a tooltip that lists the sigla of witnesses that attest that reading to materialize

As can be seen in the image immediately below, Columns 20 and 22 have a difference that involves a dot under a letter (20d vs 20a [two letters]; 22c vs 22a [one letter]), a convention in textual criticism for representing uncertain or restored readings:

Figure 23: Standalone Collation Editor (supplied sample text before normalization)

In this example the input into the Standalone Collation Editor represents letters with and without underdots as different, although a human editor might regard those correspondences as not significant for textual-criticism purposes, and therefore as candidates for normalization

An editor who decides that the presence vs absence of an underdot is not significant for collation purpose can manually normalize the forms with dots as non-significant variants of the forms without dots. The normalization can be rendered in the interface or hidden; in the image below it is exposed:

Figure 24: Standalone Collation Editor (supplied sample text after normalization)

The same text after the editor has normalized the forms with underdots as variants of those without underdots

In this sample the Standalone Collation Editor begins with single-token alignment points, that is, without the merging of adjacent readings that CollateX refers to as segmentation. An editor who decides that the phrase εφαγον τον αρτον should be regarded as a single, three-token alignment point (comparable to CollateX segmentation) can implement that merger manually within the Standalone Collation Editor interface, producing the following result:

Figure 25: Standalone Collation Editor (supplied sample text with segmentation)

The same text after the editor has merged the three tokens into a single alignment point

Interfaces for editing the alignment

Both the Relationship manager tool in Stemmaweb and the Standalone Collation Editor can be used to modify an alignment, but the interfaces they offer are based on visualizations that prioritize different features of the alignment. The critical-apparatus interface of the Standalone Collation Editor is likely to be familiar to more textual scholars than the variant graph, and its focus on an ordered sequence of individual alignment points can guide the editor through a point-by-point review of the tradition. The variant-graph interface that underlies the Stemmaweb Relationship manager, on the other hand, provides more immediate access to more types of information; for example, witness identifiers are part of the regular variant-graph display, while the Standalone Collation Editor renders them only on hover, and therefore for only one reading at a time. Stemmaweb incorporates tools for generating stemmata and exploring where they do and do not correspond to the manuscript tradition, while the Standalone Collation Editor prioritizes fine-tuning the relationships that will be reflected in an eventual rendered critical apparatus. The critical apparatus interface of the Standalone Collation Editor records places where individual witnesses lack a reading, while in the variant-graph interface of the Stemmaweb Relationship mapper, the silence of a particular witnesses at certain moments in the tradition is implicit.

Editors who find it convenient to approach a textual tradition in terms of alignment points (that is, critical-apparatus renderings) are likely to feel comfortable with the Standalone Collation Editor, which edits a collation by editing an apparatus-like view of it. Editors who find it convenient to focus on the flow of information through individual witnesses (cf. the Rhine Delta model described above) are likely to feel comfortable with the Stemmaweb Relationship mapper, where a reader can follow the labeled edges in a variant graph to see when witnesses agree or disagree and how the text of a single witness flows (to continue the riverine metaphor) through the tradition.

As the fifth and last stage of the Gothenburg model, Visualization often represents a moment of transition, where a collation tool (such as CollateX) presents its final output for human consumption. The visualizations discussed in this section, though, are not intended entirely or primarily to communicate final collation output; they serve instead as part of the Analysis (fourth) stage of the Gothenburg model, providing an opportunity for a human to modify the machine alignment before passing the result along to the Visualization stage. Visualizations in tools for editing the collation resemble final-form visualizations because they communicate alignment results to human editors, but they also need to provide affordances that allow the human to add alignment information. Editing a variant graph is closer to editing the underlying model of the alignment, while editing a critical apparatus is closer to editing a final-form rendered output. We know of no tool for editing collation information through an alignment-table interface, which is not surprising because an alignment table includes a lot of repetition (which would complicate the editing interface) and it is not generally able to model subgroups of shared readings visually.