Managing Complaint Mechanisms for Regulatory Enforcement: Evidence From Human Rights Institutions During the <scp>COVID</scp> ‐19 Pandemic
Bibliographic record
Abstract
ABSTRACT How do regulatory bodies ensure that including the beneficiaries of regulation in regulatory processes improves governance? In many regulatory arrangements, beneficiaries' “fire alarm” monitoring and reporting of targets' violations via complaint mechanisms activate regulatory bodies' enforcement role. This article theorizes how beneficiaries may misuse complaint mechanisms, undermine regulators' performance, and prompt regulators to adopt strategies within and beyond the complaint process to regulate beneficiaries' behavior. It argues regulators' assessment of the issues driving misuse and their enforcement approach (cooperative or deterrent) affect their strategies for influencing beneficiaries. Case studies of two Canadian human rights institutions, which have different enforcement approaches but experienced similarly extreme levels of beneficiary misuse during the COVID‐19 pandemic, evaluate these theoretical claims. Overall, the study illustrates potential enforcement challenges arising from using beneficiaries as intermediaries for monitoring and reporting violations and how regulating beneficiary participation may be required to improve decentralized regulatory governance.
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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 | 0.087 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".