How Regulating Risk and Eschewing Competition Can Ameliorate a Global Financial Crisis: Canada's Perspectives and Experiences
Bibliographic record
Abstract
This article identifies several key aspects of the Canadian banking regulatory regime that contribute to its stability. At the same time, it calls into question the current consensus that Canadian banking governance has been uniformly more heavily regulated than that of the United States. It is not the quantity of regulation that matters, but rather the quality. After all, the agents at the heart of the crisis in the United States were themselves highly, if inappropriately, regulated. The banks disclosed the types of instruments they used and quantified their risks. The article proceeds in a context of the overarching question of the ostensible trade-off between financial sector entrepreneurship and innovation on the one hand and stability in banking policy on the other, calling into question the assertions of law-and-economics jurists who argue that the true cost of stability in the financial sector is a less competitive and less dynamic capital market.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".