Can non‐state global governance be legitimate? An analytical framework
Bibliographic record
Abstract
Abstract In the absence of effective national and intergovernmental regulation to ameliorate global environmental and social problems, “private” alternatives have proliferated, including self‐regulation, corporate social responsibility, and public–private partnerships. Of the alternatives, “non‐state market driven” (NSMD) governance systems deserve greater attention because they offer the strongest regulation and potential to socially embed global markets. NSMD systems encourage compliance by recognizing and tracking, along the market’s supply chain, responsibly produced goods and services. They aim to establish “political legitimacy” whereby firms, social actors, and stakeholders are united into a community that accepts “shared rule as appropriate and justified.” Drawing inductively on evidence from a range of NSMD systems, and deductively on theories of institutions and learning, we develop an analytical framework and a preliminary set of causal propositions to explicate whether and how political legitimacy might be achieved. The framework corrects the existing literature’s inattention to the conditioning effects of global social structure, and its tendency to treat actor evaluations of NSMD systems as static and strategic. It identifies a three‐phase process through which NSMD systems might gain political legitimacy. It posits that a “logic of consequences” alone cannot explain actor evaluations: the explanation requires greater reference to a “logic of appropriateness” as systems progress through the phases. The framework aims to guide future empirical work to assess the potential of NSMD systems to socially embed global markets.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".