Engagement, surengagement et sous-engagement académiques au collégial : pour mieux comprendre le bien-être des étudiants
Bibliographic record
Abstract
Cet article porte sur les déséquilibres de l’engagement scolaire étudiés auprès de 2 96 étudiants collégiaux. L’étude vise à développer des mesures du surengagement et du sous-engagement ainsi qu’à vérifier l’hypothèse selon laquelle ces modes de fonctionnement sont négativement reliés au bien-être, contrairement à l’engagement scolaire qui y est positivement relié. En plus d’analyses factorielles ayant permis de valider les échelles de mesure, des analyses de régression ainsi qu’une approche par profils ont permis de confirmer la nature des liens unissant les différents modes d’engagement au bien-être personnel. Les résultats sont discutés sous l’angle de leur utilité clinique ou pédagogique potentielle.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".