Dispute settlement, labor and environmental provisions in PTAs: When will business interests shift positions?
Bibliographic record
Abstract
Abstract Some protrade business interests that are against hard enforcement of labor and environmental provisions in trade deals may end up eventually supporting it, while others stick to their initial opposition. Why? When will their positions change? The existing literature would expect protrade interests to be more or less in favor of non-trade issues in trade policies according to how dependent on the international economy they are. However, longitudinal variation in export- and import-dependence does not suffice to explain change of the sort I am interested in. I argue that the position of protrade business interests change as they accumulate experiences on the negotiation/ratification of trade deals. To probe that argument, I present two paired comparisons analyzing the position of protrade business interests as pertains to the use of sanctions to enforce labor and environmental provisions in preferential trade agreements (PTAs) signed by Canada and Australia, and by the United States (US) and European Union (EU) between 1993 and 2019. My analysis points to the overall plausibility of my hypothesis and to avenues for future research. The paper helps understand the political activity of business interests on trade and sustainable development and can shed new light on the politics behind the design of social and environmental provisions in PTAs.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".