Le bonheur collectif : une approche-population au bien-être subjectif est-elle possible ?
Bibliographic record
Abstract
Il y a un intérêt croissant pour l’utilisation d’indicateurs de bien-être subjectif pour le monitorage au niveau des populations et pour l’évaluation des politiques sociales. Les indicateurs collectifs de bien-être subjectif fournissent des informations sur la qualité de vie qui complètent d’autres indicateurs sociaux et économiques. Des recherches récentes mettent l’accent sur les conditions sociales qui contribuent au bien-être. Que disent les recherches sur une approche-population pour la promotion du bien-être? Une telle approche est-elle pertinente pour les psychologues? Cet article vise à donner un aperçu non exhaustif de la littérature qui traite des indicateurs de bien-être subjectif et de leurs implications possibles pour les politiques sociales et les interventions au sein de la population.
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 | 0.068 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".