The Role of Deliberative Mini-Publics in Improving the Deliberative Capacity of Multi-Stakeholder Initiatives
Bibliographic record
Abstract
Multi-stakeholder initiatives (MSIs)—private governance mechanisms involving firms, civil society organizations, and other actors deliberating to set rules, such as standards or codes of conduct, with which firms comply voluntarily—have become important tools for governing global business activities and the social and environmental consequences of these activities. Yet, this growth is paralleled with concerns about MSIs’ deliberative capacity, including the limited inclusion of some marginalized stakeholders, bias toward corporate interests, and, ultimately, ineffectiveness in their role as regulators. In this article, we conceptualize MSIs as deliberative systems to open the black box of the different elements that make up the MSI polity and better understand how their deliberative capacity hinges on problems in different elements. On the basis of this conceptualization, we examine how deliberative mini-publics—forums in which a randomly selected group of individuals from a particular population engage in learning and facilitated deliberations about a topic—can improve the deliberative capacity of MSIs.
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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 | 0.157 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".