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Record W2132386551 · doi:10.1111/1468-0289.665

Why didn't Canada have a banking crisis in 2008 (or in 1930, or 1907, or …)?

2014· article· en· W2132386551 on OpenAlexaffabout
Michael D. Bordo, Angela Redish, Hugh Rockoff

Bibliographic record

VenueThe Economic History Review · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFinancial systemShadow banking systemFinancial crisisSystemic riskShadow (psychology)Investment bankingInvestment (military)BusinessMoney marketFinancial regulationEconomicsMarket economyFinanceInterest ratePolitical scienceLawKeynesian economics

Abstract

fetched live from OpenAlex

The financial crisis of 2008 engulfed the banking system of the US and many large European countries. Canada was a notable exception. In this article we argue that the structure of financial systems is path‐dependent. The relative stability of the Canadian banks in the recent crisis compared to the US in our view reflected the original institutional foundations laid in place in the early nineteenth century in the two countries. The Canadian concentrated banking system that had evolved by the end of the twentieth century had absorbed the key sources of systemic risk—the mortgage market and investment banking—and was tightly regulated by one overarching regulator. In contrast, the relatively weak, fragmented, and crisis‐prone US banking system that had evolved since the early nineteenth century led to the rise of securities markets, investment banks, and money market mutual funds (the shadow banking system) combined with multiple competing regulatory authorities. The consequence was that the systemic risk that led to the crisis of 2007–8 was not contained.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.004
Scholarly communication0.0060.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.230
Teacher spread0.191 · 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 designObservational
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

Citations137
Published2014
Admission routes2
Has abstractyes

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