Bibliographic record
Abstract
espanolLos traductores conocen bien el fenomeno de la variacion terminologica, pero hace apenas veinte anos que los terminologos empezaron a interesarse por el y no hay unanimidad en torno a su definicion y su tipologia. En el frances medico son bastante numerosas las variantes morfologicas, graficas, sintacticas y morfosintacticas. La pervivencia de una nomenclatura anatomica antigua y de numerosos eponimos es otro factor que contribuye a la variacion en el frances medico, como lo hacen tambien, en menor grado, los hibridos grecolatinos y los regionalismos, algunos sinonimos y cuasi sinonimos que se utilizan indistintamente, y algunas formulas pleonasticas. EnglishTranslators are only too familiar with the phenomenon of term variation, but terminologists first took an interest in it merely 20 years ago and still do not agree on a definition or typology. Medical French contains numerous morphological, graphic, syntactic and morphosyntactic variants. The survival of an antiquated anatomical nomenclature and many eponyms is another factor contributing to variation in medical French. Other factors also contribute to a lesser degree. These include Greco-Roman hybrids and regionalisms, some synonyms and quasi-synonyms that are used indiscriminately, and some pleonastic formulas. francaisLe phenomene de la variation est bien connu des traducteurs, bien qu�il n�interesse les terminologues que depuis une vingtaine d�annees. La definition et la typologie memes du phenomene ne font pas l�unanimite. En francais medical, les variantes morphologiques, graphiques, syntaxiques et morphosyntaxiques sont relativement nombreuses. La survivance d�une ancienne nomenclature anatomique et de nombreux eponymes contribue elle aussi a la variation en francais medical. Les hybrides greco-latins et les regionalismes, certains synonymes et quasi-synonymes utilises de facon interchangeable, et quelques formules pleonastiques y participent a un moindre degre.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".