Analyse expérimentale du comportement des producteurs de céréales sèches face au risque financier dans un contexte de changement climatique
Bibliographic record
Abstract
Cette étude a un double objectif : caractériser les exploitants agricoles suivant leur niveau d’aversion au risque financier et analyser les facteurs explicatifs de leur propension à prendre un risque financier. En utilisant un système de loteries inspiré des travaux d’Allais (1953) sur 540 exploitants agricoles choisis dans le Bassin arachidier du Sénégal, les résultats montrent que 81,38 % des producteurs présentent une aversion au risque financier et seulement 8,57 % sont « risquophiles ». Les estimations avec le modèle probit binomial montrent également que la propension à prendre un risque financier diminue si l’exploitant agricole se fixe un niveau élevé de perte de production tolérable. Ces résultats suggèrent de prendre en compte les composantes de la demande dans les politiques publiques de financement et d’assurance agricole.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".