Bibliographic record
Abstract
The particular conception of copy-text proposed by W.W. Greg in his “A Rationale of Copy-Text,”especially his “distinction between the [. . .] ‘substantive’ [. . .] readings of the text, those namely that affect the author’s meaning or the essence of his expression, and others, such in general as spelling, punctuation, word division, and the like, affecting mainly its formal presentation, which [. . .] I shall call [. . .] ‘accidentals’,” has not really achieved much purchase in the field of editing medieval vernacular manuscript texts, perhaps because punctuation is often entirely or largely missing in them, word division purely scribal, and spelling subject to dialect translation from manuscript to manuscript. Nevertheless, the general procedure that Greg recommended — briefly, that “copy-text should govern (generally) in the matter of accidentals” — is the one that holds sway in the editing of medieval text though under different terminology: the usual procedure is to select one manuscript, if there are several, as the ‘base text’ for an edition and to emend its text (if at all) only in cases of substantive disagreement with other manuscripts (if a better reading exposes an error in the base manuscript) or when there is obvious error shared by all manuscripts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".