Bibliographic record
Abstract
We identify and discuss the risks of failures in governance of regulatory authorities and the actions governments and regulatory authorities can take to mitigate these risks. The mandates of regulatory authorities are to protect the public by ensuring that entities under their jurisdiction are compliant with the legislation and regulations governing their activity. It is imperative that regulatory authorities hold themselves accountable for fulfilling their responsibilities to the same standard of compliance through consistent, certain, and ethical behaviours. Risks in effectiveness of the governing legislation and regulations for regulatory authorities, in ethical behaviours of the member(s) of their governing Boards of regulatory authorities, and in effectively implementing governance principles and best practices by their governing Boards can lead to failure of governance and in fulfilling their responsibilities. Failures in any of these areas result in loss of public confidence and trust in regulatory authorities, and consequently erodes public confidence and trust in the regulated entities under their jurisdiction. A recent example of mismanagement and misappropriation of funds by the energy regulator in Alberta is a case study of the root causes of governance failures. We provide recommendations for jurisdictions and their regulatory authorities to consider in developing sound regulatory oversight that ensures failures in governance do not occur.
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.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".