Declining harmonization in maximum residue levels for pesticides
Bibliographic record
Abstract
Purpose Maximum residual limits (MRLs) for pesticides are based on science. This is true both for MRLs devised by national governments and multilaterally through the Codex. Science-based Codex MRLs are internationally harmonized to facilitate trade. Since the 1990s, an increasing number of countries have devised national MRLs and eschewed those of the Codex. These differing national standards are becoming important barriers to trade. The purpose of this paper is to explore the ramifications of these diverging MRLs for food security, investigate the reasons for the rise of national standards, and explore the role of science in regulatory processes. Design/methodology/approach The approach is an examination of the scientific basis for MRLs in the context of food safety outcomes. Findings It finds that there is no improvement in food safety from the move to national MRLs, only a loss of the benefits of trade. As all countries, along with the Codex, claim that their MRLs are based on science, suggesting that there is a need for an examination of the role of science in the making of public policy. Originality/value This study identifies a potential risk to food security for food policy makers. Given future food security challenges and that pesticides are used almost universally in conventional agriculture, trade barriers based on divergent interpretations of science need to be addressed by food policy makers.
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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 | 0.064 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".