Facteurs linguistiques liés à la connaissance des racines latines et grecques : origine et fréquence
Bibliographic record
Abstract
Dès la fin du primaire, les élèves éprouvent des difficultés avec les textes informatifs (PIRLS, 2011), dont un grand nombre de mots est composé de racines latines et grecques (ex. : Green, 2008). Des recherches récentes ont déjà identifié que les mots rares (Cervetti et al., 2015) ou abstraits (Hiebert et al., 2019) contribuaient à la mécompréhension des mots. La présente recherche descriptive vise à vérifier si l’origine des racines, la fréquence des mots ainsi que la fréquence des racines sont liées à la connaissance des racines latines et grecques chez 34 élèves francophones de 6e année du primaire. Les résultats révèlent que les élèves produisent plus d’erreurs liées au sens des racines latines que des racines grecques. Cependant, aucune relation n’a été constatée entre la fréquence des mots ou des racines ainsi que la performance des élèves au test de connaissance des racines latines et grecques.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".