Reengaging Criminology in Regulation and Governance: A Synergistic Research Agenda on Regulatory Guardianship
Bibliographic record
Abstract
ABSTRACT Recent literature calls for scholars to bridge the divide that has emerged between criminology and regulation and governance. In the current work, we propose that criminological opportunity theories provide one fruitful pathway to that end. Specifically, we introduce the notion of regulatory guardianship based on the concepts of guardians, guardian capability, and guardian willingness to intervene, and connect them to the regulation and governance literature. We demonstrate the utility of this perspective as the building blocks for improving theoretical understanding of the effectiveness of a broad range of parties engaged in compliance work in specific regulatory environments. Considering new empirical insights into regulatory guardianship in the design of future legislation and systems of oversight and accountability may also improve governance implementation and effectiveness.
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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 | 0.054 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.103 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".