Structural and Relational Bargaining Resources of Suppliers and Labor Compliance in GVCs
Bibliographic record
Abstract
MNEs as lead firms of global value chains have implemented the private labor regulation by enforcing a private labor standard upon its suppliers. Lead firms work with highly diverse suppliers in terms of their operational capacities, but prior research that prioritizes the lead firm perspective offers limited insights into how supplier heterogeneity can influence supplier’s compliance performance. This study examines the relationship between structural and relational resources of suppliers and their compliance outcomes by analyzing 1,311 labor compliance audits of 619 suppliers from Aeriva, a North American apparel lead firm, across seven countries. Our findings reveal that relationally resourceful suppliers, which possess internal systems to identify and address compliance risks, are more likely to exhibit higher labor compliance. Contrary to expectations, having large production capacities—an indication of structural barging resources—did not impact the level of labor compliance. The study also suggests that being relationally resourceful is independent of how long a supplier has been working with the lead firm.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".