Bibliographic record
Abstract
Le bien-être des habitants est un des objectifs des politiques publiques. Aussi, est-il bienvenu de proposer des méthodes qui placent le bien-être au centre des diagnostics territoriaux. Cet article présente un outil cartographique participatif, nommé Escapat, de mise en œuvre d’un diagnostic territorial de manière collective. Il permet de déterminer les éléments essentiels de bien-être pour les habitants du territoire sur lequel il est utilisé. Dans cet article, Escapat est utilisé dans trois espaces ruraux isolés de la Drôme et d’Ardèche (France) auprès de groupes d’habitants. L’expérimentation de l’outil Escapat souligne sa capacité à activer la gouvernance territoriale en tant que support et espace de discussion entre habitants. Il révèle aussi les dimensions socio-spatiales du bien-être. De nouvelles questions émergent en matière de recherche et de prospective territoriale : logiques d’accumulation des éléments préférentiels de bien-être, disponibilité et nombre de services de la vie courante en milieu rural isolé.
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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".