L’expertise comme pouvoir : le cas des organisations de retraités face aux politiques publiques en France et aux États-Unis
Bibliographic record
Abstract
Les organisations américaines et françaises de retraités poursuivent des stratégies cognitives variées pour orienter leurs activités militantes dans le domaine des politiques publiques. Malgré d’indéniables points communs, des différences significatives existent entre les organisations des deux pays. Ainsi, l’expertise mobilisée par les organisations américaines se construit généralement autour d’une logique de professionnalisation. En France, au contraire, les organisations de retraités fondent leur action sur un certain « amateurisme cognitif », et ce bien qu’une tendance à la professionnalisation de l’expertise semble se faire jour. À cette recherche de professionnalisation s’ajoute une logique d’expertise partisane ( advocacy ) qui s’étend dans les deux pays. Mais, dans l’immédiat, l’élaboration de « contre-pouvoirs sociaux » dans le domaine de l’analyse et de l’élaboration des politiques publiques semble beaucoup plus développée aux États-Unis qu’en France.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".