Can Non-State Governance 'Ratchet Up' Global Standards? Assessing Indirect and Evolutionary Potential
Bibliographic record
Abstract
In the past decade and a half, interest in non-state market driven (NSMD) governance has grown so that it is now championed to cover virtually every major global problem including forest deterioration, fisheries depletion, mining destruction, tourism, industrial factory conditions in developing countries, e-waste, and climate change. Existing research has revealed a troubling puzzle: support has either been strongest among firms and within regions where regulations are relatively high or it has emerged in niche markets that, by definition, cannot generate global standards to which all production must adhere. What is evolutionary potential of NSMD to move beyond market separation to “ratchet up” global standards? Answering this question requires that the next generation of research focus on three potentially more powerful indirect effects that current support for NSMD systems may trigger: support from less regulated firms as market uptake occurs; learning and norm generation of NSMD systems that may influence more authoritative domestic and intergovernmental policy arenas; and the impacts that standards in one sector may influence regulations in others, such as occurs between forestry and agriculture. We draw on cases from developing and developed countries to illustrates and assess our argument.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".