2007b) 'Harvesting Public Policy? Private Influence on Agricultural Trade Policy in Canada
Bibliographic record
Abstract
inquiry, for the comments of all the workshop participants, for helpful suggestions from James Rude, and for the research assistance of Jesse Helmer and Tim Sayle. After a full day of unedifying debate on agricultural trade policy in November 2005, just before the Hong Kong ministerial conference of the World Trade Organization (WTO), the Canadian House of Commons gave unanimous approval to a motion instructing negotiators to seek increased market access abroad for agricultural exporters while ensuring that the protection from imports offered the supply managed sectors did not change (Canada, 2005c: 9960, 10017). This schizophrenic instruction pursued at the behest of the producers of milk, eggs and poultry in eastern Canada who compete with imports, and modified at the behest of western export-oriented grain farmers, limited the ability of Canadian ministers to be full participants in the negotiations, and it did not serve the wider interests of Canadians. Private harvesting of public policy takes place in plain view, yet it is still mysterious. Knowing more about how private actors attempt to influence trade policy is consequential for global governance, where problematic domestic support and understanding may be the chief stumbling block to renovation and extension of an open liberal multilateral trading system, and for the operation of the trading system, where
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".