Governance gaps in eradicating forced labor: From global to domestic supply chains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A growing body of scholarship analyzes the emergence and resilience of forced labor in developing countries within global value chains. However, little is known about how forced labor arises within domestic supply chains concentrated within national borders, producing products for domestic consumption. We conduct one of the first studies of forced labor in domestic supply chains, through a cross‐industry comparison of the regulatory gaps surrounding forced labor in the United Kingdom. We find that understanding the dynamics of forced labor in domestic supply chains requires us to conceptually modify the global value chain framework to understand similarities and differences across these contexts. We conclude that addressing the governance gaps that surround forced labor will require scholars and policymakers to carefully refine their thinking about how we might design operative governance that effectively engages with local variation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it