Transcribing and Collating for Digital Stemmatology. The Case of Troilus and Criseyde
Bibliographic record
Abstract
This paper explores the decisions involved in a digital stemmatology project. Traditionally, transcription, collation, and the creation of a stemma have been processes linked to the edition of texts. However, stemmatology on its own can provide valuable insight to further the understanding of literary works since it sheds light on their production process. The research on the textual tradition of Troilus and Criseyde offers the possibility to reconsider what are the difficulties and implications of a digital project of this sort. I focus particularly on transcription and collation. Transcription can be affected by aspects such as the availability of high-quality reproductions of manuscripts and early printed editions. Then, in order to produce useful transcriptions that serve the purpose of the project, the level of detail has to be established. It is important to find a balance between the overwhelmingly detailed and scarcity that could result in unfruitful transcripts. In regard to collation, which is the process of identifying variants, I mention the characteristics and purpose of a base-text. It is essential to understand what is a variant and how to work with them so that reliable stemmatta can be produced. Thus, I examine the case of two readings present in my research. By the end, I provide a brief example of how a phylogenetic tree can help us understand the distribution of variants and the relationships that the witnesses of Troilus and Criseyde bear. With that, I also hope that the usefulness of digital stemmatology is made evident.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".