Cajetan’s Biblical Commentaries: Motive and Method
Bibliographic record
Abstract
comptes rendus 263 to rearrange and produce original creations.Keith Sidwell offers similar advice on editing Neo-Latin texts, but shifts the discourse to early modern Ireland and other less traversed areas of Latinity.On the question of whether to correct and standardize orthography, he favours keeping all the variant spellings, punctuation, and markings of original printed texts while providing an explanatory apparatus.He also insists that any edition or translation of a Neo-Latin work requires detailed commentary and that collaboration is essential, as the classical philologist has to understand in depth the particular historical context.Kallendorf's and Sidwell's advice and lists of resources and websites for Neo-Latin texts is an essential starting point for serious study in the field.This book-in particular, all the chapters on different genres-decisively gives the lie to the prejudices against Neo-Latin literature that have hampered the field for so long.Critics, including C. S. Lewis, have dismissed Neo-Latin compositions as slavish imitations and insincere showpieces devoid of content and obsessed only with hyper-classicism.On the contrary, as vividly shown in these essays, Neo-Latin literature abounded in variety, creativity, and a depth of thought connected as much to an author's own world as to models of classical literature.anthony f. d'elia
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".