Querying Variants: Boccaccio’s ‘Commedia’ and Data-Models
Bibliographic record
Abstract
This paper presents the methodology and the results of an analytical study of the three witnesses of Dante’s Commedia copied by Giovanni Boccaccio, focusing on the importance of their digital accessibility. These extraordinary materials allow us to further our knowledge of Boccaccio’s cultural trajectory as a scribe and as an author, and could be useful for the study of the textual tradition of Dante’s Commedia. In the first section of the paper, the manuscripts and their role in previous scholarship are introduced. A thorough analysis of a choice of variants is then offered, applying specific categories for organizing the varia lectio. This taxonomy shows how fundamental it is to combine the methodological tools for studying copies (as usual in medieval philology) and those for studying author’s manuscripts (as usual in modern philology) in dealing with the three manuscripts of Boccaccio’s Commedia: in fact, the comparative analysis of the three manuscripts has much to reveal not only of their genetic relationship but also of Boccaccio’s editorial practices. Furthermore, the analytic categories inform the computational model behind the web application ‘La Commedia di Boccaccio’, created for accessing and querying the variants. The model, implemented in a relational database, allows for the systematic management of different features of textual variations, distinguishing readings and their relationships, without setting a base text. The paper closes on a view to repurposing the model for handling other textual transmissions, working at the intersection between textual criticism and information technology.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".