Perceptions des producteurs de foin sur l’assurance récolte indicielle au Québec
Bibliographic record
Abstract
L’assurance récolte foins au Québec est un programme gouvernemental qui compense les pertes de récoltes à partir d’indices météorologiques. Cet article montre, à l’aide d’une méthodologie mixte combinant l’analyse d’entrevues semi-dirigées et de statistiques, que le design et les modalités de livraison de ce programme ont une incidence sur ses résultats. Il existe un décalage entre les résultats positifs du programme, mesurés en termes d’adhésion et des indemnités versées, et la perception négative du programme par les producteurs de foin, qui s’explique par leur mauvaise compréhension du risque de base (corrélation imparfaite entre les pertes estimées et réelles). Par ailleurs, certains producteurs développent des comportements non désirés (par exemple, la maximisation de leurs indemnités).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".