Les enseignants et le dictionnaire : sentiments, attitudes motivationnelles, connaissances déclarées et pratiques personnelles d’utilisation
Bibliographic record
Abstract
sum Le prsent article documente le type de sentiments et les attitudes motivationnelles de 300 enseignants qubcois du primaire et du secondaire face aux dictionnaires papier et lectroniques, ainsi que leurs connaissances dictionnairiques et pratiques d'utilisation dclares. Les rsultats rvlent que les enseignants entretiennent des sentiments positifs envers les dictionnaires. De plus, ils se sentent gnralement comptents dans l'utilisation de cet ouvrage et y accordent un degr de valeur lev, en plus de dtenir des ouvrages varis. Cependant, les connaissances et usages dclars demeurent relativement rudimentaires, ce qui souligne les besoins de formation pour une meilleure exploitation personnelle et didactique des dictionnaires.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".