The Case for Constructive Ambiguity in a Regulated System: Canadian Banks and the 'Too Big to Fail' Problem
Bibliographic record
Abstract
This brief focuses on the purported Canadian virtues of risk aversion and regulatory caution in light of one important characteristic of the banking system: it is dominated by only five large banks that are “too big to fail.” I address the issue using a concept – ambiguity – which is often mentioned but relatively neglected analytically in the scholarly literature on bank regulation. I argue that the capacity of the Canadian banking system to successfully navigate the “too big to fail” problem presents an instance in which this form of ambiguity may contribute to helpful dynamics in the regulatory landscape, in that it can attenuate the moral hazard dilemma posed by banks that are “too big to fail.” I discuss the ways in which the refusal to permit mergers among the large Canadian banks in the late 1990s shaped the constructive ambiguity animating the relationships among the banks, the Bank of Canada, and bank regulators. I will argue that this policy decision both enhanced the credibility of the government’s constructive ambiguity and attenuated the moral hazard implications of banks that are “too big to fail” in Canada. I conclude with a discussion of the implications of this analysis for regulatory initiatives going forward.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".