Bibliographic record
Abstract
Nous présentons dans cet article les critères permettant de distinguer les différentes acceptions d’un mot polysémique. Ce travail préalable de distinction – nous parlons de lexémisation – est indispensable à la description du mot dans le dictionnaire. Nous prenons comme exemple le mot polysémique breton vannetais keuz ‘regret’ parce que d’une part, les acceptions de keuz présentent de notables différences avec leurs équivalents français et que, d’autre part, le vannetais connaît tout un groupe d’acceptions qui sont inconnues, semble-t-il, en breton KLT et n’ont pas d’équivalents en français. Notre cadre théorique est la théorie Sens-Texte.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Lexicographic analysis of the senses of a Breton word; linguistics (word polysemy, not the metaresearch sense).
It analyzes lexical polysemy in Breton, not research itself.
Lexical semantics of a Breton polysemous word for dictionary description; linguistics domain.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".