Climat scolaire et bien-être à l'école
Bibliographic record
Abstract
Comprend : Du « climat scolaire » : définitions, effets et politiques publiques / Éric Debarbieux - L'école à l'ère du 2.0. Climat scolaire et cyberviolence / Catherine Blaya - Le climat scolaire vu par les chefs d'établissement du second degré public / Benjamin Beaumont - Où fait-il bon enseigner ? / Cédric Afsa - Le climat scolaire perçu par les collégiens / Tamara Hubert - L'absentéisme des élèves soumis à l'obligation scolaire. Un lien étroit avec le climat scolaire et le bien-être des élèves / Sophie Cristofoli - Les relations entre milieu social, climat scolaire et réussite scolaire en Israël. Les hypothèses de compensation, de médiation et de modération / Ruth Berkowitz, Hagit Glickman, Rami Benbenishty, Elisheva Ben-Artzi, Tal Raz, Nurit Lipshtadt, Ron Avit Astor - Élèves handicapés ou porteurs de maladies chroniques. Perception de leur vie et de leur bien-être au collège / Emmanuelle Godeau, Mariane Sentenac, Dibia Liz Pacoricona Alfaro, Virginie Ehlinger - Le bien-être des élèves à l'école et au collège. Validation d'une échelle multidimensionnelle, analyses descriptives et différentielles / Philippe Guimard, Fabien Bacro, Séverine Ferrière, Agnès Florin, Tiphaine Gaudonville, Hué Thanh Ngo - Relations professeurs-élèves en lycée. Trois stratégies d'enseignants mises en débat / Hélène Veyrac, Julie Blanc - Satisfaction professionnelle des enseignants du secondaire. Quelles différences entre public et privé ? / Nathalie Billaudeau, Marie-Noël Vercambre-Jacquot
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".