Managing Regulatory Risks and Defining the Parameters of Blame: A Focus on the Australian Prudential Regulation Authority
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it