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Record W3092180096 · doi:10.17132/2693-3179.1088

The Canadian Lenders Assurance Facility (Canada GFC)

2020· article· en· W3092180096 on OpenAlexaboutno aff
Claire Simon

Bibliographic record

VenueJournal of Financial Crises · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinanceDebtDisadvantageGovernment (linguistics)DisadvantagedFinancial systemEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Following a meeting of Group of Seven leaders in October 2008, the Canadian Minister of Finance announced the creation of a new Canadian Lenders Assurance Facility (CLAF). The facility enabled federally regulated deposit-taking financial institutions to access government insurance of up to three years on newly issued senior unsecured wholesale debt. This mirrored similar programs in other countries to ensure that Canadian financial institutions were not competitively disadvantaged in the wholesale debt market at a time when most developed countries were guaranteeing their banks’ debt. This competitive disadvantage never materialized, and the facility was allowed to expire on December 31, 2009, without ever being used.

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)0.003
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.002
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0510.005

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 head0.035
GPT teacher head0.216
Teacher spread0.182 · 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
Published2020
Admission routes1
Has abstractyes

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