Private regulatory capture via harmonization: An analysis of global retailer regulatory intermediaries
Bibliographic record
Abstract
Abstract Studies using the Regulatory–Intermediary–Target (RIT) framework have examined a variety of forms of regulatory capture, including how targets capture intermediaries (T➔I) and how intermediaries capture regulators (I➔R). Little attention has been paid to why and how regulators themselves might engage in capture. Yet such a scenario is likely in transnational governance settings characterized by regulatory competition and conflict, as well as power differentials between different types of private regulators (non‐governmental organizations, multinational corporations, and business associations). This paper elucidates why and how a private regulator might capture another private regulator via a regulatory intermediary: R1➔I➔R2. Drawing on interview and archival data, I examine three industry‐driven regulatory intermediaries created to harmonize private labor codes of conduct and ethical audit processes. These are founded and governed by a small group of retail trade associations and global retailers who also fulfill the role of private regulators (R1). My analysis reveals that the creation of these intermediaries is driven by global retailers’ reliance on standardization, low transaction costs, and regulatory harmonization across all aspects of their operations. It further reveals how the harmonization platforms are designed to leverage global retailers’ market power and evolve from regulatory intermediaries into de facto regulators that supplant existing private regulators (R2), and thereby capture transnational governance of consumer product supply chains. The article concludes by discussing contributions, implications, and avenues for future research.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".