Regulatory patterns in international pork trade and similarity with the EU SPS/TBT standards
Bibliographic record
Abstract
Aim of study: With the increasing protagonism of non-tariff measures (NTMs) in trade policy, better indexes are needed to depict the prevalence and similarity of NTMs across countries for further use in trade impact assessments.Area of study: Worldwide, with special focus on the European Union (EU)Material and methods: Using the TRAINS database on NTMs, we calculated and proposed some indicators, stressing both regulatory intensity and diversity, as well as similarity of regulatory patterns between trade partners. Our application focuses on pork trade and main importers, amongst which, the EU is singled out.Main results: We found a high level of heterogeneity in NTMs’ application, both, in the number and variety of measures. The bilateral similarity was relatively low, such as only 30% of sanitary and phytosanitary measures (SPS) and 20% of technical barriers to trade were shared, providing ground and incentive for discussing trade policy harmonization. Our analysis suggests that SPS regulations prevail in those sectors and countries more engaged in trade, while a negative correlation with tariffs raises protectionism concerns. Our bilateral indicators rank country pairs according to the similarity of their regulatory patterns. The EU, for instance, is closer in SPS regulations to China or USA than to Canada or New Zealand, which will require actions in the context of the bilateral trade agreements in course.Research highlights: The low similarity of regulatory patterns evidence the challenges faced by policy makers to streamline technical regulations. For an accurate representation of regulatory patterns and their impact on trade, both uni- and bilateral indicators need to be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".