Mitigating Non-Tariff Measures in Agriculture: Preferential Trade Agreements and Conversations
Bibliographic record
Abstract
Regulatory divergence between countries is creating barriers to market access in international agri-food trade, becoming what are known as non-tariff measures (NTMs). NTMs create friction in international trade, increasing fulfillment costs and, if sufficiently burdensome, pose a very real threat to global food security. More countries, including Canada, are turning to preferential trade agreements (PTAs) to liberalize trade. The Comprehensive Economic and Trade Agreement (CETA) between Canada and the European Union was heralded as comprehensive in its coverage, reducing or eliminating tariffs in virtually all aspects of trade. Overall bilateral trade has increased since CETA came into force but not for many of Canada’s agri-food exporters. CETA’s tariff-focused agenda did little to mitigate the NTMs impeding many Canadian agri-food exports. NTMs are not unique to Canada-EU trade. They are an increasing factor impeding world food trade as more governments are basing policy decisions on ideological or political factors rather than sound science. As a result, regulatory divergence in NTMs is widening among a greater number of countries. The agri-food trade system becomes less predictable, riskier and more volatile. For international trade, it has been described as a slow death by 1,000 regulations. The only means to address regulatory divergence is to facilitate regulatory convergence. This is not an easy task given the number of multi-disciplinary stakeholders involved, both domestic and international. While PTAs are not well equipped to legislatively force convergence, they do provide informal and formal opportunities for building networks, strengthening relationships and opening communication channels that can foster and facilitate regulatory convergence. Every opportunity to do so, whether bilaterally, through PTAs or multilaterally, must be taken advantage of.
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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 | 0.048 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 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".