Bibliographic record
Abstract
In recent years, many states have undertaken to green their free trade agreements (FTA). As the pace of this evolution towards greener trade relations continues to accelerate, it has also been met with resistance. The inclusion of environmental commitments in FTAs has sometimes been dismissed as an attempt by high-income countries to level the playing field for their market actors by raising environmental standards abroad. Against this background, this article aims to investigate what underlying motive(s) (high-income) states pursue when they negotiate environmental provisions. Using the United States-Mexico-Canada Agreement (USMCA) as a case study, it is argued that it is possible to rely on the legalization of these commitments to unravel treaty parties’ motives for negotiating such rules in the first place. In the case of the USMCA, it is found that the agreement’s environmental commitments could be interpreted as mirroring concern either for the environment or for unfair foreign competition. A closer look at the negotiation process leading to the adoption of the agreement suggests that it was mainly – although certainly not exclusively – out of environmental concerns that stringent environmental commitments were included in the USMCA. free trade agreements, US trade relations, economic integration, United States-Mexico- Canada Agreement, asymmetries of power, negotiation of treaties, environmental provisions, green protectionism, trade and sustainable development, legalization of international commitments
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".