Effects Of The Canadian Wheat Board On The U.S. Wheat Industry
Bibliographic record
Abstract
The practices of the Canadian Wheat Board (CWB) are an important issue in U.S. - Canada trade disputes and WTO negotiations. This study analyzes the CWB?s effect on U.S. producers by reviewing findings from previous research and developing models to analyze CWB wheat exports to the United States and the competitive structure of Canadian wheat exports in the world market. U.S. grain producers could benefit from the removal of the CWB as the United States could become more competitive in export markets. However, elimination of the CWB could also result in an increase in Canadian wheat exports to the United States as Canadian producers near the border could sell directly to the United States to take advantage of market opportunities. The net effect is difficult to quantify. The net benefits may be significant in the short run, but reduced significantly in the long run. Similarly, if Canada reforms its wheat board by eliminating trade-distorting subsidies, the United States may increase its exports and Canadian exports to the United States may increase. The net benefits from reforming the CWB could be greater than those from eliminating it. However, the United States could benefit the most from complete elimination of state trading enterprises (STEs), since they have elements distorting trade flows and the United States competes with several STEs in the world wheat market.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".