Involving Civil Society in the Implementation of Social Provisions in Trade Agreements: Comparing the US and EU Approach in the Case of South Korea
Bibliographic record
Abstract
The last few years have seen an increase of both free trade agreements (FTAs) and social provisions therein, such as the standards from the International Labour Organisation (ILO). The US and the European Union (EU) are two of the biggest proponents of the trade-labour linkage. While the US practice is characterized by a ‘conditional’ approach, the EU’s approach is seen as ‘promotional’. Nonetheless, both foresee the possibility for civil society – such as unions, business organisations and academics - to monitor the implementation of social provisions. By focusing on the trade agreements of the US and the EU with South Korea, this paper assesses to what extent these civil society monitoring mechanisms differ and to what extent they can be effective in the long run. Methodologically the paper combines an analysis of the legislative texts of the trade agreement and of official documents produced by the mechanisms on the one hand and expert interviews on the other hand. The explorative study shows that the following factors are important for long term impact: fixed participants, funding, feedback of the governments on the advice of the mechanisms and strong institutionalisation.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".