Bibliographic record
Abstract
NAFTA is turning 25 years old and is in desperate need of modernization. The contentious yet game-changing multilateral free trade agreement has had no shortage of political detractors and proponents over the years, but on May 18, 2017 President Donald Trump officially signaled its renegotiation to the U.S. Congress. Agricultural producers from all three countries have greatly benefited from free trade with their North American neighbors, but Canada insists on maintaining their restrictive supply management system and tariff rate quotas on dairy imports. The Dairy Farmers of Canada (DFC) fear a NAFTA renegotiation could not only threaten their lucrative system but also remove an important check against unfettered subsidization of U.S. dairy producers. The DFC have repeatedly criticized the magnitude of U.S. dairy subsidies, especially during the Farm Bill legislation process every four years, attributing rising milk supply glut and lower global dairy prices to their size. Although subsidies do aid U.S. producers’ bottom line and global competitiveness, they do little to explain efficiency discrepancies between nations or justify Canadian protectionism. Using empirical research and statistical analysis, the validity of these arguments will be tested to better determine the future of dairy production within NAFTA and beyond.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".