Linking Trade, Environment, and Social Cohesion: NAFTA Experiences, Global Challenges
Bibliographic record
Abstract
Forging the trade-environment-social cohesion link - global challenges, North American experiences, John J. Kirton and Virginia W. MacLaren. Linking Trade, Environment, and Social Values - The Global and NAFTA Experiences: From trade liberalization to sustainable development - the challenges of integrated global governance, Pierre Marc Johnson The new interface agenda among trade, environment, and social cohesion, William A. Dymond Embedded ecologism and institutional inequality - linking trade, environment, and social cohesion in the G8, John J. Kirton Winning together - the NAFTA trade-environment record, John J. Kirton. Investor Protection - Evaluating the NAFTA Chapter II Model: The masked ball of NAFTA chapter II - foreign investors, local environmentalists, government officials, and disguised motives, Sanford E. Gaines Environmental expropriation under NAFTA chapter II - the phantom menace, Julie Soloway Investment and the environment - multilateral and North American perspectives, Konrad von Moltke. Environmental Protection - Evaluating the NAFTA Commission for Environmental Co-operation Model: Stormy weather - the recent history of the citizen submission process of the North American agreement on environmental co-operation, Christopher Tollefson Public participation within NAFTA's environmental agreement - the Mexican experience, Gustavo Alan s Ortega Articles 14 and 15 of the North American agreement on the environmental co-operation - intent of the founders, Serena Wilson. Worker Protection - Evaluating the NAFTA Commission for Labour Co-operation Model: Civil society and the North American agreement on labour co-operation, Kevin Banks Giving teeth to NAFTA's labour side agreement, Jonathan Graubart Understanding the environmental effects of trade - some lessons from NAFTA, Scott Vaughan Sustainability assessments of trade agreements - global approaches, Sarah Richardson Concern for the environmental effects of trade in Canadian communities - evidence from local indicator reports, Virginia W. MacLaren Using indicators to engage the community in sustainability debates, Noel Keough Development and usability of a system for environment and health indicators - a case study, David L. Buckeridge and Carl G. Amrhein. Concluding Reflections: Fix it or nix it? - will the NAFTA model survive?, Sylvia Ostry Conclusions, Virginia W. Maclaren and John J. Kirton.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".