Comprendre et développer le bénévolat en loisir dans un univers technique et « clientéliste »
Bibliographic record
Abstract
RésuméLes comportements et les contributions des bénévoles en loisir sont, essentiellement, le fruit d’un équilibre entre l’action libre et volontaire et les exigences de performance des organisations et des personnes desservies. Appuyé sur les résultats d’une enquête québécoise visant à éclairer les pratiques gestionnaire de soutien et de développement du bénévolat, le présent article traite principalement des rapports des organisations avec les bénévoles et expose les opinions et les perceptions de ces derniers et des professionnels. Il discute, particulièrement, la question des rôles des uns et des autres, des attentes des uns par rapport aux autres, de la vision de l’encadrement selon les uns et les autres et expose les pistes et les défis de nouvelles pratiques gestionnaires envers le bénévolat et les bénévoles en loisir.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".