Mettre en mouvement les agriculteurs victimes des pesticides
Bibliographic record
Abstract
À partir du cas d’une association d’agriculteurs victimes des pesticides en France, nous proposons de rendre compte du rôle que jouent les coalitions politiques dans l’émergence et la pérennisation des mobilisations de victimes. Nous mettons en évidence le rôle tenu par une organisation proche de l’écologie politique dans la conversion de cas épars d’agriculteurs malades en une cause collective et dans la création d’une coalition autour de cette cause. Cette coalition a rapidement subi une série d’épreuves liées à la distance politique séparant ses membres. Nous identifions les facteurs qui lui permettent d’y résister et montrons que c’est au cours de ces épreuves que l’identité des victimes est négociée. Plus largement, cet article illustre l’intérêt d’analyser le travail de coalition dans la durée pour comprendre l’évolution des mobilisations victimaires.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".