Policy Harmonization and Adjustment in the North American Agricultural and Food Industry
Bibliographic record
Abstract
This is the fifth publication emanating from a series of annual workshops designed to enhance communication among the three partners in the NAFTA Agreement.The workshops bring together business and interest group representatives, government officials and academics from Mexico, the United States and Canada to develop economic information related to agricultural and food markets.The primary purpose of the workshops and the publication is to contribute to lessening of trade tensions among the three countries, and thereby head off wasteful trade disputes.Previous workshops have focused on grain and dairy disputes, and analyzed the meaning and conditions for "policy harmonization".Each of these workshops was characterized by a macro and public policy focus because the public domain is the obvious arena in which trade tensions are played out.But the individual components of the private sector and, therefore, private sector adjustment are very much affected by trade agreements and policy change.In many respects, the private sector is the vehicle of change.This perspective of the agricultural/food industry and trade policy was the primary focus of the fifth workshop.Since Mexico was the site for this workshop, the program emphasized adjustment within the agricultural and food industry in Mexico.
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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 | 0.003 | 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 | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".