Agri‐environmental Programmes and Trade Negotiations in the United States and the European Union Programmes agroenvironnementaux et négociations commerciales aux États‐Unis et dans l’Union européenne Agrarumweltprogramme und Verhandlungen im Außenhandel in den USA und der europäischen Union
Bibliographic record
Abstract
summary Agri‐environmental Programmes and Trade Negotiations in the United States and the European Union In both the European Union and the United States, the public has raised concerns over the damaging effects of modern agriculture. Both regions have developed agri‐environmental programmes (AEPs), but the conceptual background is quite different. We argue that the EU programmes treat agriculture and the natural world as complementary, while the US programmes primarily treat them as substitutes. As a result, the EU pays farmers for the production of environmental amenities from farming, while many of the US programmes generate environmental externalities by limiting agriculture. The US approach is much more site‐specific, which may imply larger environmental gains per dollar, but less flexibility to offset reductions in production subsidies. The EU AEPs, by contrast, are very broad, but are adopted by many farmers who do not receive production subsidies, creating a new group of subsidy recipients who may have their own objectives for the trade talks. In part because of these broad‐based AEP and rural development programmes, the EU appears to have a freer hand in removing trade‐distorting price supports. However, by encouraging smaller, remote farmers to produce high‐value market goods e.g. organic, the EU may have created a domestic lobby for protecting these markets from increased international competition.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".