The Case for Constructive Ambiguity in a Regulated System: Canadian Banks and the 'Too Big to Fail' Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it