Bibliographic record
Abstract
This paper provides a framework for thinking about agricultural policy, why and how it is introduced, and how it changes over time. This framework suggests that agricultural policy will be influenced by both concerns for efficiency and lobbying. While agricultural policy will not always be effective, it will be relatively stable, at least in terms of its broad outlines. Underlying this broad stability, however, will be considerable small‐scale change as program and policy details shift in response to a changing environment. When policy changes in a major way, which it almost always will, the shift will be abrupt—a punctuation. These abrupt changes come as attention is eventually paid to areas and/or issues that are increasingly understood to be not working. While there is considerable room for economic analysis in the policy process, it will not be the main driver; this role belongs to politics—the ability to change the discourse around a policy issue in such a way that different evaluations and interpretations of the policy and its impact are created. Based on the analysis in this paper, it is argued that supply management is more likely to see significant change than business risk management programs, since more attention seems to be currently directed at the former issue. It is also argued that although proponents of local food, organic production, and urban agriculture have had some success at getting attention focused on these issues, this success will not translate into any major policy changes, in part because markets for these products are developing and appear to be working reasonably well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".