Bibliographic record
Abstract
La notion de capital social et les concepts afférents s’appliquent à de nombreuses disciplines, dont la santé. Les définitions varient, la théorie fait l’objet de débats et les techniques de mesure ne sont pas harmonisées. Les chercheurs se demandent en particulier si le capital social est une notion pertinente sur un plan individuel ou collectif. En abordant les aspects théoriques du capital social, cet article entend désamorcer partiellement le débat sur le niveau de pertinence de cette notion. Il suffirait d’opérer une distinction entre les discussions cherchant à savoir où se situe le capital social (si c’est un attribut des individus ou des relations) et celles qui se demandent comment il est mesuré et comment on y accède. Il suggère que le mieux serait de conceptualiser le capital social et les notions connexes de réseaux sociaux, de soutien social et de cohésion sociale comme un continuum allant de l’individuel au collectif pour la définition et la pertinence. Cette contribution étudie ensuite ce qui permet d’établir des associations avec l’état de santé, et traite des considérations de politiques publiques en se référant en particulier au cas des personnes âgées.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".