Managing Regulatory Risks and Defining the Parameters of Blame: A Focus on the Australian Prudential Regulation Authority
Bibliographic record
Abstract
Risk‐based regulation is a new arrival in the lexicon of risk and regulation. Regulators in Australia, Canada, and the UK have begun developing systems and processes to assess the probability and impact of compliance failures by regulated firms, and to adjust their relationship with firms accordingly. This article explores the motivations for, and key elements of, the risk‐based frameworks of one of those regulators, the Australian Prudential Regulation Authority (APRA). It broadens out from this case study to argue first, that risk‐based regulation goes hand in hand with the technique of “meta” regulation, the regulation of the firm's own internal self regulation, and will both fuel and be fueled by any trend towards the latter. Second, it argues that risk‐based frameworks are not risk‐free: whilst they seek to manage risks they inevitably introduce their own. Third, risk‐based regulatory frameworks have the potential both to expose and obscure key sociopolitical and socioeconomic choices as to the amount or types of regulatory failures that an agency will tolerate, and which in effect it is requiring society to tolerate. “Risk based frameworks” are attempt to define what are acceptable “failures” and what are not, and thus to define the parameters of blame.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| 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".