Bibliographic record
Abstract
The Technical Note focuses on sizeable mortgage exposures and persistent housing market imbalances. The review evaluated oversight of deposit-taking institutions (DTIs) in federal jurisdiction, as well as in British Columbia and Québec. There are many good, well-functioning mechanisms in place for cooperation. Areas that warrant improvement about DTI regulation and supervision include policy development, especially between Office of the Superintendent of Financial Institutions (OSFI) and Autorité des marchés financiers, coordination of data collection, and exchange of useful prudential information between different agencies. The authorities should explore how to remove barriers that prevent close and meaningful cooperation. Risk weights on mortgage lending appear too low for insured mortgages and may not be sufficiently through-the-cycle for banks using the Internal Ratings Based approach. A common forbearance definition and monitoring framework for credit risk (in the context of loan restructuring by DTIs should be adopted across all jurisdictions in Canada. A similar definition, in line with the guidance of the Basel Committee for Banking Supervision, and consistent data will help improve risk monitoring especially given the importance of debt restructuring for managing problem real estate exposures. Finally, OSFI’s guideline on asset pledging should ensure enough unencumbered assets to support the claim of depositors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.337 | 0.104 |
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".