Quelle est votre conception du bienêtre ? Quatre pistes pour une étude et une pratique éclairée du bienêtre en contexte scolaire
Bibliographic record
Abstract
L’intérêt croissant pour le bienêtre en milieu scolaire a incontestablement contribué à approfondir notre compréhension de ce phénomène. Cependant, en parallèle, plusieurs critiques émergent concernant les raccourcis conceptuels qui, mal empruntés, peuvent nuire au champ de recherche. Nous proposons une réflexion qui souligne quatre aspects clés nécessitant une vigilance accrue : la sélection minutieuse des concepts et des termes utilisés, la prise en compte des références philosophiques sous-jacentes, l’harmonisation entre la définition du concept et sa méthode de mesure, ainsi que l’alignement entre les besoins des environnements, les objectifs identifiés et les actions concrètes entreprises.
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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".