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. <strong>Résumé</strong> Cet article explore les décisions prises dans un projet de stemmatologie numérique. Traditionnellement, la transcription, la collation et la création d’un stemma étaient des processus liés à l’édition de textes. Cependant, la stemmatologie elle-même peut fournir des informations précieuses qui contribuent à une meilleure compréhension d’œuvres littéraires, puisqu’elle éclaircit leur processus de production. La recherche sur la tradition textuelle de Troilus et Criseyde offre la possibilité de réexaminer les difficultés et les implications d’un projet numérique de ce genre. Je me concentre en particulier sur la transcription et la collation. La transcription peut être affectée par des aspects tels que la disponibilité de reproductions de haute qualité de manuscrits et d’éditions imprimées anciennes. Ensuite, pour produire des transcriptions utiles qui atteignent le but du projet, il faut établir le niveau de détails. Il est important de trouver l’équilibre entre des transcriptions massivement détaillées et celles qui sont peu détaillées pour éviter des résultats infructueux. En ce qui concerne la collation, le processus d’identifier des variantes, je mentionne les caractéristiques et buts d’un texte de base. Il est essentiel de comprendre ce qui c’est une variante, ainsi que comprendre la façon dont il faut la traiter pour que des stemmatta fiables puissent être produits. Par conséquent, j’examine le cas de deux lectures présentes dans ma recherche. Je fournis un exemple de la façon dont un arbre phylogénétique peut nous aider à comprendre la distribution de variantes et les relations que témoignent Troilus et Criseyde. Dans cet esprit, j’espère aussi rendre évidente l’utilité de la stemmatologie numérique. <strong>Mots-clés:</strong> transcription; collation; stemmatologie; phylogénétique; Chaucer; Troilus
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".