« Il faut que ça tienne ! » : étudier le bénévolat à la lumière des dynamiques d’attachement et de détachement
Bibliographic record
Abstract
L’article propose d’explorer le bénévolat à travers les attachements et les détachements des bénévoles à une cause et à un projet de manière générale. Nous analyserons cette dynamique en mobilisant une approche constitutive de la communication organisationnelle (CCO) afin de comprendre ce qui lie, mais aussi ce qui délie les différents acteurs participant à un projet de bénévolat. L’analyse révèle plusieurs sources d’attachements et de détachements qui amènent les bénévoles à s’investir à différents degrés, voire même à quitter le projet. De plus, nous explorons en quoi certains liens (tels que la cause, le quartier et la socialisation des bénévoles) transforment la définition et le mode d’existence du projet de bénévolat et en font sa force (et sa faiblesse). En ce sens, notre étude se concentre sur les liens qui attachent et détachent, et ce faisant font tenir un projet de bénévolat au fil des passions qui meuvent les participants.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".