A quest for balance:changing design of labor provisions in trade agreements in the US, the EU and beyond
Bibliographic record
Abstract
This thesis seeks to make sense of the change in the design of labor provisions in EU and US trade agreements over the years. What drives change? When are those changes weaker or stronger? In coining my "balance of promotion" argument, I argue that there is a causal relation between the strengthening of the commercial provisions of a preferential trade agreement (PTA) and the labor provisions therein. The rationale of my theory is that the greater the promotion of protection to exporters and multinationals via PTAs relative to the domestic protection of workers' rights in the negotiating partners, the greater the mobilization of domestic interests in favor of a stronger promotion of the rights of workers in those PTAs. I then hypothesize the degrees of change (low, moderate, high) according to the degree of mobilization of domestic interests in favor and against stronger labor provisions in PTAs. A process tracing of the EU and the US in the period between 2000 and 2013 confirms my hypotheses. To start exploring whether my theoretical argument is valid beyond the EU and the US, I also conduct plausibility probes of Canada and the Asia-Pacific region (Australia and Japan). My research offers an original contribution to the literature on the design of sustainable provisions in trade agreements.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".