Forging a New Path in English-French Lexicography: Guy Miège in Relation to Robert Sherwood
Bibliographic record
Abstract
Abstract From 1632 to the late 1670s, the English-French lexicographic scene was monopolized by Robert Sherwood’s Dictionaire Anglois & François, which was annexed to Randle Cotgrave’s second edition of the Dictionarie of the French and English Tongues and reissued in all subsequent editions (1650, 1660, and 1673). Sherwood’s work would have been the source of choice for any lexicographer wanting to compile a new English-French dictionary. However, for Guy Miège, the author of A New Dictionary French and English, with another English and French published in 1677, Sherwood’s dictionary seems to have been a minor influence at best. What the relation between them sets forth is the significantly new range and originality of Miège’s work in comparison, as demonstrated in this study.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".