Bibliographic record
Abstract
How the environmental provisions in US preferential trade agreements affect both the environmental policies of trading partners and the effectiveness of multilateral environmental agreements. As trade negotiations within the World Trade Organization seem permanently stalled, countries turn increasingly to preferential trade agreements (PTAs) between smaller groups of nations. Many of these PTAs incorporate environmental provisions, some of which require trading partners to enact new domestic environmental laws, and use the enforcement mechanisms available within trade agreements as tools for environmental protection. In Greening through Trade, Sikina Jinnah and Jean-Frédéric Morin provide the first detailed examination of how the environmental provisions in US preferential trade agreements affect both the environmental policies of trading partners and the effectiveness of multilateral environmental agreements. They do so through a combination of in-depth qualitative case studies and quantitative analysis of an original dataset of 688 global PTAs. Jinnah and Morin explore the effects of linkages between PTAs and environmental treaties and the diffusion of environmental norms and policy through PTAs. Centrally, they argue that US trade agreements can serve as mechanisms both to export environmental policies to trading partner nations and third-party countries and to enhance the effectiveness of multilateral environmental agreements by strengthening their enforcement capacity. They caution that PTAs are not a panacea for environmental governance; deeper problems of unsustainable consumption and differential power dynamics between trading partners must be carefully navigated in deploying trade agreements for environmental protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".