International regulatory and oversight trends in financial consumer protection: What can be gleaned from the UK, Portuguese, Irish and Canadian experiences?
Bibliographic record
Abstract
Political and economic events with global ramifications continue to afflict new regulatory regimes. These regimes are being developed to address events such as COVID-19 and climate change, and developments in areas such as diversity and inclusion, cybersecurity, ESG (Environmental, Social, and Governance), cryptocurrency, etc. These developments have forced organisations and regulators alike, including financial sector regulators, to assess their impact on the way regulators perform their work. This paper describes some of the trends related to financial conduct oversight that have emerged recently in the UK, Portugal, Ireland and Canada and how conduct authorities in these countries have responded to address these trends. It endeavours to shine a light on initiatives that have been taken by some regulators to advance conduct oversight and the protection of consumers in their respective countries. It is meant to sensitise and inform jurisdictions (whose conduct frameworks may not be as developed as those studied for this paper) to the progress that is being made in the area of conduct policy and supervision. It is hoped that it could also serve as a potential preparatory tool for compliance practitioners whose conduct authorities may be contemplating similar changes to their frameworks.
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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 | 0.001 | 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 | 0.001 |
| 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".