Description prédictionnairique de trois mots du français québécois : francophone, anglophone et allophone
Bibliographic record
Abstract
Monsieur Bernard Quemada, dans un article intitulé « La nouvelle lexicographie », suggérait une phase « de rédaction prédictionnairique » à celle de la rédaction d'un article de dictionnaire. À l'Université de Sherbrooke, nous avons mis en œuvre une telle description prédictionnairique. La BANQUE DE DONNÉES TEXTUELLES DE SHERBROOKE contient plus de six millions de mots et elle est composée de divers textes : oraux, littéraires et non littéraires. Afin de décrire les unités lexicales contenues dans cette banque de données, nous avons mis au point une fiche prédictionnairique pour les substantif s fréquents qui s'y trouvent. Je me propose de situer notre travail prédictionnairique et de l'illustrer au moyen de quelques termes fréquents de notre banque et caractéristiques de certains textes québécois. Je choisirai ceux relatifs à l'expression d'une langue : francophone (par opposition à français ), anglophone (par opposition à anglais ) et allophone .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".