Le conseil aux exploitations agricoles pour faciliter l'innovation : entre encadrement et accompagnement
Bibliographic record
Abstract
Le conseil en agriculture est un service important pour améliorer les performances des exploitations et les capacités des agriculteurs à innover. Toutefois, son efficacité est régulièrement questionnée. Dans ce chapitre, nous abordons l’évolution du conseil en agriculture, pour montrer tant l’évolution des dispositifs que celle des méthodes pour fournir du conseil. Il existe différentes approches de conseil, mobilisant des principes différents. On peut citer l’aide à la décision, la résolution de problèmes, le renforcement de capacités visant l’autonomisation des agriculteurs, ou l’accompagnement d’un projet individuel ou collectif. Le choix d’une approche dépend de la nature du problème à traiter et des solutions à mettre en oeuvre mais aussi des capacités des conseillers, des objectifs que se fixent les organisations de conseil, et enfin des mécanismes de gouvernance et de financement du conseil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; both teacher heads agree on what is shown here.
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".