Trade agreements and labour standards clauses: Explaining labour standards developments through a qualitative comparative analysis of US free trade agreements
Bibliographic record
Abstract
Whereas a number of studies have been conducted to investigate causal relations between individual conditions (e.g. trade relations and labour standards), there is a lack of consensus among practitioners and scholars about the conditions that favour or cause labour standards improvements and, specifically, it is still unclear whether the increasing pervasiveness of Free Trade Agreements (FTAs) is conducive to enhancing labour conditions. The aim of this study is to shed light on whether labour clauses in FTAs are conducive to better labour standard practices, whether the content of a clause makes a difference, and whether changes have anything to do with other (external) pressures that play a role in changing labour standards. The main argument of the article is that FTAs do not play a determinant role in improving labour standards in signatory states. The analysis is done by looking at 13 FTAs signed by the United States with 19 countries. The United States is chosen because of its relatively extensive collection of FTAs including different conditions on labour standards. The empirical dataset is analysed with Qualitative Comparative Analysis (QCA) method, which permits to trace the combined effect of independent variables rather than to focus on the direct and individual causality with each of them.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".