MétaCan
Menu
Back to cohort
Record W1842329897 · doi:10.1111/jmcb.12193

Building Stable Mortgage Markets: Lessons from Canada's Experience

2015· article· en· W1842329897 on OpenAlexaboutno aff
Allan Crawford

Bibliographic record

VenueJournal of money credit and banking · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsUnderwritingMortgage underwritingShared appreciation mortgageBustSecondary mortgage marketMortgage insuranceCollateralized mortgage obligationDebtBoomInterest rateBusinessFinancial systemLoan-to-value ratioFinanceMonetary policyEconomicsMonetary economicsInsurance policyCasualty insurance

Abstract

fetched live from OpenAlex

The global financial crisis illustrated the high costs of boom–bust cycles in housing and mortgage markets and the importance of implementing policy frameworks that mitigate the risk of these events. This article discusses elements of Canada's policy framework that contributed to the relatively good performance of its mortgage market in recent years, including supervisory practices and mortgage underwriting standards. Lender recourse and the nondeductibility of mortgage interest payments played a complementary role. Ongoing policy challenges are also identified, including the need for monitoring to ensure the current prolonged period of low interest rates does not lead to levels of debt and house prices that create future instability in housing and mortgage markets.

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)0.007
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 score0.108
Threshold uncertainty score0.780

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.005
Scholarly communication0.0100.003
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.045
GPT teacher head0.241
Teacher spread0.195 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of money credit and bankingSame topicHousing Market and EconomicsFrench-language works237,207