What do First-tier Suppliers do for Labor Compliance in Global Value Chains?
Bibliographic record
Abstract
Global value chains have become increasingly complex, involving multiple tiers of suppliers. This makes it difficult for the lead firm alone to regulate labor practices in the chains. Recognizing that first-tier suppliers have become progressively more capable of sharing value chain orchestration with lead firms, we examine what these suppliers do to support the lead firm’s labor regulation. Our case study focuses on two Korean MNCs that are major first-tier suppliers in the apparel and electronics industries. We find that to comply with the lead firm’s labor standard (primary agency role), these suppliers ran self-audit programs and fostered cross-functional coordination. To cascade the lead firm’s regulation to sub-suppliers (secondary agency role), these suppliers employed coercive and consultative strategies. In exercising these double agency roles, they exhibited competence as regulatory intermediaries—specifically, compliance expertise, operational capacity, and enforcement legitimacy vis-à-vis sub-suppliers. While navigating challenging lead firm and host country contexts, the first-tier suppliers supported the lead firm’s labor regulation through the strategy of good enough compliance: a level of compliance that posed minimal risk to the lead firms and permitted the suppliers not to sacrifice production goals when they collide with those of compliance.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".