Benchmarking global supply chains: the power of the ‘ethical audit’ regime
Bibliographic record
Abstract
Abstract This article critically investigates the growing power and effectiveness of the ‘ethical’ compliance audit regime. Over the last decade, audits have evolved from a tool for companies to track internal organisational performance into a transnational governing mechanism to measure and strengthen corporate accountability globally and shape corporate responsibility norms. Drawing on original interviews, we assess the effectiveness of supply chain benchmarks and audits in promoting environmental and social improvements in global retail supply chains. Two principal arguments emerge from our analysis. First, that audits can be best understood as a productive form of power, which codifies and legitimates retail corporations’ poor social and environmental records, and shapes state approaches to supply chain governance. Second, that growing public and government trust in audit metrics ends up concealing real problems in global supply chains. Retailers are, in fact, auditing only small portions of supply chains, omitting the portions of supply chains where labour and environmental abuse are most likely to take place. Furthermore, the audit regime tends to address labour and environmental issues very unevenly, since ‘people’ are more difficult to classify and verify through numbers than capital and product quality.
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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 | 0.127 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".