Portrait des fautes d’orthographe dans des textes d’étudiants québécois
Bibliographic record
Abstract
Discussion Orthographe grammaticaleLes fautes sont majoritairement du côté de l'orthographe grammaticale (70 %).La faute d'orthographe la plus courante concerne l'accord en nombre.Cette faute n'est pas liée à la complexité de la règle mais au fait que les marques de nombre à l'écrit, muettes, ne correspondent pas au fonctionnement réel de la langue française. Le participe passéLes fautes d'accord du participe passé arrive en 2 e position parmi les fautes d'orthographe grammaticale.La réforme actuelle sur les accords du participe passé cible un problème important.Notre corpus permet d'arriver à la même conclusion que Leroy et Leroy (1995) : la majorité des fautes d'accord du participe passé est l'absence d'accord.Toutefois, avec la réforme proposée, le scripteur se trouvera toujours devant un choix : faut-il ou non accorder le participe passé?Or l'accord en nombre est la source principale de faute d'orthographe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".