Using Experiments to Design and Evaluate the <scp>CAP</scp>: Insights from an Expert Panel
Bibliographic record
Abstract
Summary Over the last twenty years the Common Agricultural Policy (CAP) has considerably evolved, by introducing new objectives and instruments to address the increasing number of challenges ahead. These changes call for the use of innovative tools to analyse agricultural policy design and evaluation. During the last European Association of Agricultural Economics conference, a panel of experts presented their points of view on how experiments can enhance the CAP evaluation toolkit. In this article we summarise the main insights emerging during this session. We present a review of the different existing experimental approaches followed by some examples of their application in the study of the CAP and a discussion of the potential hurdles to the use of experimental results to design actual policies. From the different contributions it emerges that experimental approaches may represent effective tools to improve the design and evaluation of the CAP. However, a potential hurdle to their wider use is that experimental results often fail to be confirmed when applied to large‐scale policy interventions. In this article we discuss some potential solutions to the main problems affecting the scalability of experimental results, and we provide some insights on how experiments can help to improve the CAP further.
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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 | 0.469 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".