Profils linguistiques, cognitifs et motivationnels d’étudiants du postsecondaire faibles en français écrit
Bibliographic record
Abstract
Le présent article s'intéresse aux caractéristiques d'étudiantes et d'étudiants des ordres postsecondaires diagnostiqués comme particulièrement faibles en français écrit. Trois aspects différents sont proposés pour tenter de dégager le profil de ces sujets faibles, celui des habiletés et des connaissances linguistiques, celui des habiletés et des connaissances discursives, celui des perceptions et des attributions. Les résultats montrent des sujets désorientés par rapport aux apprentissages scolaires antérieurs d'ordre linguistique, confondant règles et procédures; des sujets recourant systématiquement au processus de révision en situation de rédaction et des sujets qui se perçoivent comme relativement compétents et responsables de leur succès comme de leurs échecs face à des tâches d'écriture.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".