Comment obtenir des données détaillées quant aux compétences d’élèves de 1re année en lecture et en écriture ?
Bibliographic record
Abstract
Dans cet article, il est question de l’instrumentation et des modalités d’analyse mises en œuvre pour obtenir des données concernant : 1) les compétences en lecture des élèves ; 2) leurs compétences en écriture.Pour ce qui est de la compétence à lire, en contexte québécois, il est attendu que les enseignants considèrent en lecture quatre dimensions essentielles désignées comme la compréhension, l’interprétation, la réaction et l’appréciation (Ministère de l’Éducation, du Loisir et du Sport, 2009). Pour ce qui est de la compétence à écrire, quatre composantes de l’écriture sont considérées : la conceptualisation, l’énonciation, l’encodage et la matérialisation (Montésinos-Gelet, 2013, adapté de Levelt, 1989, et de Berninger & Swanson, 1994).
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".