Crowdsourcing Compliance: The Use of WikiRate to Promote Corporate Supply Chain Transparency
Bibliographic record
Abstract
Abstract This article analyzes the use of crowdsourcing to promote corporate sustainability by assessing compliance with supply chain disclosure laws. It draws on a case study of WikiRate.org as a novel example of crowdsourcing compliance with respect to the UK Modern Slavery Act and U.S. conflict minerals legislation (section 1502 of the Dodd-Frank Financial Reform Act). WikiRate is an open research platform whose mission is to crowdsource better companies by motivating corporations to be transparent about their environmental, social, and governance performance. In particular, WikiRate’s projects on modern slavery and conflict minerals harness the power of citizens to evaluate the quality of corporate disclosures produced in accordance with these laws. Following an analysis of its projects on modern slavery and conflict minerals, I evaluate the challenges of using crowdsourcing to assess legal compliance, including the potential manipulation of data and the difficulty of relying on non-expert citizens to assess complex information in corporate disclosures. I argue that one must identify the appropriate “crowd” that would be most capable of assessing compliance with a given law. While crowdsourcing platforms such as WikiRate invite a broad range of stakeholders to assess compliance, the reality is that only a limited set of individuals may be able to meaningfully participate given the complexity of supply chain disclosures. Thus, “expertsourcing” may be a more appropriate tool for assessing compliance with certain laws as it limits participation to citizens with specialized expertise.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".