Disease Management, Economic Incentives and Trade
Bibliographic record
Abstract
Barriers to trade can be imposed if a threat of importing a disease exists. Sanitary and phytosanitary (SPS) measures have historically been applied on a national basis, even though regions in an exporting country may have very different disease profiles. The World Trade Organization's 1995 Agreement on SPS Measures included a provision for exports from disease-free subnational areas. Regionalization has been explored in depth by many countries from a scientific disease control perspective, but not from an economic perspective, and negotiations have been exclusively science-focused. As yet, little progress has been made towards correcting this provision. This article examines the question of creating a sustainable subnational disease-free area approved for export from an economic perspective. The analysis shows that there may be significant benefits from applying regionalization to international trade, but these benefits are not guaranteed. Recognition of economic incentives provides the key to creating sustainable disease-free subnational regions. In particular, removing the incentive to smuggle between regions is an essential requirement of an exporter's domestic policy. Economic incentives have largely been ignored both by the responsible domestic agencies and by international negotiators, but until the question of economic incentives is included in the international agenda, little progress can be expected.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".