Dialogue and Coordination: How Hybrid Models Can Strengthen Labor Standards Enforcement
Bibliographic record
Abstract
This article examines the factors that limit and support the capacity of developing states to regulate labor in the public and private spheres, as well as the role of international parties in strengthening that capacity. The purpose is to better understand the potential for a more coordinated approach informed by hybrid models of enforcement, which can contribute to closing regulatory gaps. Fieldwork was carried out in the garment sectors in South Africa and Lesotho during 2018, including 20 semi-structured interviews with industry stakeholders representing government, business, and labor. Findings indicate that the developing state has an important role to play in facilitating a more coordinated approach between systems of enforcement, including public and private enforcement agencies, national development agencies, manufacturers, buyers, and unions. The case studies indicate the potential of such an approach to, for example, improve inspection quality, accountability, and transparency. The state can play an active role in facilitating a hybrid approach to regulation that involves both state and non-state actors, with dialogue and coordination at the core of addressing broader challenges for enforcement.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".