Regulator coherence, capacity, and collaboration: applying lessons from Canada and the UK for fintech regulatory sandboxes
Bibliographic record
Abstract
ABSTRACT This paper studies the fintech regulatory sandbox initiatives by the Canadian Securities Administrators and the UK's Financial Conduct Authority and explores the critical success factors revealed by those initiatives and how they might implicate other similar initiatives in Canada, and particularly considering the specific Canadian constitutional federalism. The analysis draws lessons from the two sandboxes for regulatory success and strength in regulating sandboxes for financial technologies and argues that regulatory coherence, capacity, and collaboration are indispensable to fintech regulatory sandboxes because incorporating them in the design and implementation of fintech sandboxes would engender regulator success at supporting fintech innovation within safe guardrails. We also rely on the lesson learned from the two initiatives from studies published by the regulators themselves as well as by other scholars who have undertaken studies of these and other sandboxes. Examining these lessons helps guide the development of further regulatory sandboxes for fintech in Canada and for other technologies.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".