The environment and international trade negotiations : developing country stakes
Bibliographic record
Abstract
Introduction D.Tussie SECTION I: CASE-STUDIES The Case of a Renewable Natural Resource: Timber Extraction and Trade P.Saez Agriculture and the Environment in Developing Countries: The Challenge of Trade Liberalisation G.Gutman Environment-related Voluntary Market Upgrading Initiatives and International Trade: Eco-labelling Schemes and the ISO 14.000 Series P.da Motta Veiga International Pressure and Environmental Performance: The Experience of South African Exporters L.Bethlehem SECTION II: GENERAL ISSUES The International Negotiation of PPMs: Possible, Appropriate, Convenient? D.Tussie & P.Vasquez Lessons from Trade Theory for Environmental Economics P.Sen SECTION III: INTERNATIONAL ENVIRONMENTAL GOVERNANCE Global Governance and the Comparative Political Advantage of Regional Cooperation H.Hveem Trade Restrictions for the Global Environment: The Case of the Montreal Protocol J.Krueger Lessons from the Mexican Environmental Experience: First Results from NAFTA C.Schatan Regional Integration and Building Blocks: The Case of Mercosur D.Tussie & P.Vasquez Environmental Cooperation in ASEAN F.Wiebe Conclusions: The Environmental and International Trade Negotiations: Open Loops in the Developing World D.Tussie
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".