Heterogeneity Index of Trade and Actual Heterogeneity Index – the case of maximum residue levels (MRLs) for pesticides
Bibliographic record
Abstract
Non-tariff measures (NTMs) beyond traditional trade policy instruments define the requirements that importing countries imposed on foreign products. Due to differences across countries, requirements for supplying foreign markets can lead to trade costs and thus hamper international trade. In this paper, we introduce two regulatory heterogeneity indexes which are subsequently applied to the case maximum residue levels (MRLs) of pesticides. The Heterogeneity Index of Trade (HIT) reflects the respective differences across countries based on the assumption that the mere fact of difference in requirements causes trade costs. Taking the HIT index as a starting point, the Actual Heterogeneity Index (AHI) specially considers the situation where the requirements demanded by the importing country are stricter than those of the exporting country. The focuses is on the pesticide MRLs that the EU27 and 10 trade partner countries (Argentina, Australia, Brazil, Canada, China, Japan, New Zealand, Russia and the US) apply on a set of agri-food products (cheese, beef, pig meat, potatoes, tomatoes, apples and pears, aubergines, peppers, maize, barley and rape seed). In particular, we take the EU export perspective as the benchmark for the comparison and calculate the indexes. The indexes identify if the respective MRLs are similar or dissimilar, equal, stricter or more lenient, and the results of our analysis thus point out potential areas for negotiating equivalence or other strategies in order to overcome the possible trade-restricting impact of diverging MRLs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".