Keeping the Genie in the Bottle: Grading the Regulation of Canadian Financial Institutions
Bibliographic record
Abstract
The Canadian financial sector made it through the recent global credit crisis in better shape than most. Still the government undertook extraordinary measures to support the soundness of Canadian financial institutions. Fortunately, Canadians learned the lessons of the world banking crisis at lower cost than others. They may not be so lucky the next time. Canada’s approach to regulation includes many features that have been effective in insulating its financial sector from major shocks. Its principles-based approach has proven more adaptable to emerging financial innovations than the rules-based approaches as adopted in the U.S. By favouring permission over prohibition, it has allowed beneficial financial innovations to thrive, while leaving regulators able to step in when innovations appear harmful to the stability of the system. On the whole, Canada’s regulatory approach is, put simply, simpler and reduces the costs of compliance and enforcement. Significantly, it has remained immune from the toxic political influences that overshadow U.S. regulation. None of this guarantees that the Canadian approach to regulation is fail-proof. The Canadian financial sector has a few large banks – some with assets ranging up to 50% of GDP – who could be categorized as “too big to fail.” Deposit insurance rates remain low and insurer’s reserves are not sufficient to shield the Canadian public from the costs of institutional failure. Despite the good job in fostering a stable environment, Canadian regulators must still face a number of issues. Each financial crisis is different and future crises are always over the horizon. Success in avoiding the brunt of the last crisis does not guarantee that Canadian financial institutions will escape unscathed from the next one. Also, fast paced innovation puts regulators in a continual game of catch-up. The rapid growth of shadow banks and over-the-counter derivatives contributed to the last crisis and the issues they raise have yet to be resolved. Finally, the success of international efforts to reverse “too big to fail” by allowing troubled financial institutions to fail safely cannot be assured. It requires authorities to close failing institutions promptly but history suggests that delay may appeal to regulators. They may hope that an institution, if given time, can recover. They may also fear fuelling a financial crisis by repeating the distress unleashed by the failures in the last crisis. With no chance for a trial run, regulators may be forced to bailout failing institutions in the heat of a crisis. To prevent such an outcome, regulators must strengthen measures to ensure that major institutions are too safe to fail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".