Bibliographic record
Abstract
Although the past few decades have seen numerous cases of human rights violations within corporate supply chains, companies are frequently not held accountable for the abuses because there is a significant governance gap in the regulation of corporate activity abroad. In response, governments have begun to pass mandatory disclosure laws that require companies to release detailed information on their supply chains in the hopes that these laws will create pressure that will improve corporate accountability. In this paper, we argue that supply chain disclosure regimes are unlikely to have a large effect on consumer behavior, and as a result, their effectiveness at reducing human rights abuses will likely be limited. This is not only because scholarship on mandatory disclosure regimes in other areas has suggested that these regimes are frequently unsuccessful, but also because these problems are likely to be exacerbated in the human rights context. We argue that this is due to the fact that supply chain disclosures do not provide information on actual products, the information in the disclosures only provides weak proxies for human rights outcomes, and the risks associated with supply chains vary dramatically across industries. In order to test our argument, we engaged a leading market research firm to field a series of experiments that were designed to test how well consumers understand supply chain disclosures. In our experiments, the nationally representative sample of respondents consistently rated disclosures reporting low levels of due diligence almost as highly as disclosures that reported a high level of due diligence. Based on these results, we argue that consumer-oriented supply chain disclosure regimes designed to improve corporate human rights behavior should be reconsidered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.161 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".