L'utilité de l'enseignement du registre familier au primaire
Bibliographic record
Abstract
L’enseignement des notions de base en français fait partie intégrante du travail des enseignantes au primaire. En effet, celles-ci doivent donner des explications sur l’alphabet, sur quelques accords, sur le vocabulaire, etc. En matière de vocabulaire, si la plupart des enseignantes l’enseignent, il n’existe pas, au sein du corps enseignant, de consensus sur le type de vocabulaire à présenter aux apprenants. D’ailleurs, il semblerait, selon notre expérience, que plusieurs enseignantes mettent à l’écart les mots appartenant au registre familier et qu’elles n’enseignent que les mots appartenant aux registres standard et soutenu. Pourtant, occulter le registre familier revient à passer sous silence le vocabulaire que les Québécois utilisent au quotidien, ce qui mènerait les élèves à penser que les mots employés en dehors de l’école sont incorrects et que les Québécois parlent mal. C’est donc, entre autres, pour cette raison qu’il est on ne peut plus important que les enseignantes incluent la notion de registres de langue dans leur enseignement.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".