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Record W2960369006 · doi:10.22495/jgr_v4_i4_c1_p8

From trial to triumph: How Canada’s past financial crises helped shape a superior regulatory system

2015· article· en· W2960369006 on OpenAlexaboutno aff
Lawrie Savage

Bibliographic record

VenueJournal of Governance and Regulation · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial crisisFinancial regulationDeposit insuranceGlobal financial systemFinancial systemSystemic riskFinanceShock (circulatory)BusinessEconomicsFinancial market

Abstract

fetched live from OpenAlex

As anyone paying attention during the 2008–2009 financial crisis is aware, the Canadian financial system weathered the storm uniquely well. Exactly why Canada’s system remained so comparatively stable, while so many other foreign systems broke down, is a question that remains largely unsettled. One explanation may be that the regulatory system that emerged from a very specific history of prior crises had both prepared Canada well for such a crisis, and responded effectively as the crisis unfolded. But the very regulatory system that provided stability in recent years may also be at risk of becoming warped by its own success, with regulators so emboldened by the acclaim for their recent achievements that they overreach to ensure their track record remains unblemished in the future. The stunning collapse of a pair of western Canadian banks, a number of major Canadian trust companies and several insurance companies, as well as some other precarious near misses in the 1980s and 1990s, were a shock to the financial regulatory system, highlighting deficiencies that would be addressed with new regulations and, most notably, the creation of the Office of the Superintendent of Financial Institutions (OSFI). Canada’s centralized regulatory approach, through the OSFI and just four other major regulatory bodies, has proved both more elegant and effective than, for instance, the more complicated, more convoluted and more decentralized American financial-oversight system. But some regulated companies, insurers in particular, have long maintained that the concentration of power in Canada’s large banks has resulted in a one-size-fits-all regulatory approach that does not offer a relatively lighter burden for smaller institutions, potentially stifling growth. In other words, an over-emphasis on stability may be hampering market efficiency. Nor is there any economic evidence to shed light on whether those and other costs of regulating stability are justified by the costs spared by avoiding instability. Received wisdom would naturally assume that avoiding certain institutional collapses are worth any cost, but of course there must be some limits to that logic. To be clear, Canada’s regulatory model almost certainly appears to be a better-functioning one than that of many in its peer group, and the OSFI approach is gaining acceptance by many countries, particularly in emerging markets that are implementing cohesive regulatory systems for the first time, using the Canadian framework as a template. This does not, however, mean that Canada’s regulatory system cannot still be refined and improved. Suggestions for improvement include: the possibility of creating an industry-based collaboration committee — similar to the regulators’ Financial Institutions Supervisory Committee — that would monitor industry risk over time; the modernization of the Winding-up and Restructuring Act, conceived more than a century ago, to address the modern reality of immense and complex institutions of today, providing regulators the flexibility to resolve such entities when they become troubled; and the strengthening of board structures for large institutions, which remain much as they were in the 1980s, including the possibility of appointing permanent, full-time, independent directors and requirements for boards to better train directors and utilize outside expertise when warranted. Canada’s regulatory system is arguably one of the most effective in existence, but its success through the recent financial crisis is no guarantee that it will be sufficiently prepared for the next

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0500.031
Scholarly communication0.0290.007
Open science0.0040.007
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.027
GPT teacher head0.256
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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