Petits corpus oraux bilingues et plurilingues – enjeux théoriques et méthodologiques
Bibliographic record
Abstract
Cette contribution présente des types de petits corpus très hétérogènes, qui ne sont toujours pas clairement définis : les petits corpus oraux bilingues et plurilingues. Elle envisage d’abord de donner un aperçu de petits corpus et discute l’insécurité terminologique du domaine des corpus bi- et plurilingues. Ensuite, les bases de données de petits corpus bilingues Talkbank, HZSK, Sketchengine et CLAPOTY sont brièvement décrites pour discuter les possibilités de visualisation ainsi que de mise en disposition des données de corpus. Finalement, un exemple concret est présenté : le corpus FM, un petit corpus bilingue français-anglais d’une variété du français canadien, parlée au Manitoba.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".