Private regulation, public policy, and the perils of adverse ontological selection
Bibliographic record
Abstract
Abstract What problems can private regulatory governance solve, and what role should public policy play? Despite access to the same empirical evidence, the current scholarship on private governance offers widely divergent answers to these questions. Through a critical review, this paper details five ontologically distinct academic logics – calculated strategic behavior; learning and experimentalist processes; political institutionalism; global value chain and convention theory; and neo‐Gramscian accounts – that offer divergent conclusions based on the particular facets of private governance they illuminate, while ignoring those they obfuscate. In this crowded marketplace of ideas, scholars and practitioners are in danger of adverse ontological selection whereby certain approaches and insights are systematically ignored and certain problem conceptions are prioritized over others. As a corrective, we encourage scholars to make their assumptions explicit, and occasionally switch between logics, to better understand private governance's problem‐solving potential and its interactions with public policy.
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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 | 0.077 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.091 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".