Bibliographic record
Abstract
This chapter presents the history of the lexicography of the Romance languages, from the eighteenth century to the present day. In practice, emphasis is placed on French, Italian, and Spanish, together with Portuguese and Romanian. Behind these languages of the first rank, there are other languages, which will as far as possible be mentioned in this discussion. Historians of the Romance languages distinguish the Gallo-Romance linguistic domain (comprising French, Francoprovençal, Occitan, and Gascon), the Italo-Romance domain (Italian, Friulian, Ladin, and Romansh), and the Ibero-Romance languages (Spanish, Portuguese, Galician, Asturian, Aragonese, and Catalan), to which I add the Romanian domain (divided grosso modo into two main varieties, Daco-Romanian and Aromanian) and Sardic. If one likewise takes account of the geolinguistic varieties present within these various linguistic domains, the number of Romance language varieties capable of being made the objects of lexicographical description is considerable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".