Agri-environmental schemes: Adverse selection, information structure and delegation
Bibliographic record
Abstract
Ce travail analyse des formes alternatives de mesures agri-environnementales et l'influence de leur forme sur l'efficacité du mécanisme. Les auteurs étudient en particulier la question du ciblage spatial et de la délégation dans un contexte d'information asymétrique. Ils modélisent tout d'abord le contrat optimal en présence de sélection adverse. Ils soulignent dans ce cadre l'arbitrage entre rentes d'information et efficacité allocative. L'impact du ciblage des mesures agri-environnementales est ensuite étudié. Pour les auteurs, une structure plus désagrégée de l'information tend à augmenter l'effort demandé aux agents par le régulateur. Elle peut également impliquer des rentes d'information plus élevées et réduire la contribution nette de certains agents. Enfin, les conséquences de la délégation de l'autorité du principal à une échelle intermédiare sont examinées. Les auteurs montrent que le ciblage spatial des mesures, quand il est combiné à la délégation, conduit à des effets contrastés sur les paiements des agriculteurs.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".