Governing Global Supply Chain Sustainability through the Ethical Audit Regime
Bibliographic record
Abstract
Over the past two decades multinational corporations have been expanding ‘ethical’ audit programs with the stated aim of reducing the risk of sourcing from suppliers with poor practices. A wave of government regulation—such as the California Transparency in Supply Chains Act (2012) and the UK Modern Slavery Act (2015)—has enhanced the legitimacy of auditing as a tool to govern labor and environmental standards in global supply chains, backed by a broad range of civil society actors championing audits as a way of promoting corporate accountability. The growing adoption of auditing as a governance tool is a puzzling trend, given two decades of evidence that audit programs generally fail to detect or correct labor and environmental problems in global supply chains. Drawing on original field research, this article shows that in spite of its growing legitimacy and traction among government and civil society actors, the audit regime continues to respond to and protect industry commercial interests. Conceptually, the article challenges prevailing characterizations of the audit regime as a technical, neutral, and benign tool of supply chain governance, and highlights its embeddedness in struggles over the legitimacy and effectiveness of the industry-led privatization of global governance.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".