International Framework Agreements for Workers’ Rights? Insights from River Rich Cambodia
Bibliographic record
Abstract
The international framework agreement concluded between the Spanish fashion retailer Inditex SA and the International Textile, Garment and Leather Workers Federation (ITGLWF) was lauded as the first IFA to apply to an outsourced apparel supply chain. Central to the implementation approach of both parties is an understanding that for progress to be made in the advancement of core labour standards in producing countries, compliance efforts need to be rooted in the promotion of freedom of association, dialogue and collective bargaining. This approach has informed the interventions of the ITGLWF and the multinational Inditex SA to resolve trade union recognition disputes in a number of developing countries. This article presents and critically examines an early test case of this approach at a knitwear factory in Cambodia. Drawing on empirical research, we find that the intervention resulted in the resolution of a recognition dispute, which led to significant membership gains for a local union and the removal of fixed duration contracts. However, the article raises questions about the effectiveness and sustainability of such an approach for advancing freedom of association and collective bargaining throughout a multinational apparel supply chain, particularly in the current global economic climate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".