<scp>C</scp>anada's Housing Bubble Story: Mortgage Securitization, the State, and the Global Financial Crisis
Bibliographic record
Abstract
Abstract Canada's experience during and after the financial crisis appears to distinguish it from its international peers.Canadian real estate sales and values experienced record increases since the global financial crisis emerged in 2008, rather than declines, andCanada did not witness any bank failures. The dominant trope concerningCanada's financial and housing markets is that they are sound, prudent, appropriately regulated and ‘boring but effective’. It is widely assumed thatCanadian banks did not need, nor receive, a ‘bailout’, that mortgage lending standards remained high, and that the securitization of mortgages was not widespread. The truth, however, does not accord with this mainstream view. In fact, theCanadian financial and housing markets reveal marked similarities with their international peers.Canada's banks needed, and received, a substantial ‘bailout’, while federal policies before and after the financial crisis resulted in the massive growth of mortgage securitization and record household indebtedness. This article documents the growth ofCanada's housing bubble, the history of mortgage securitization, and of government policies implemented before and after the crisis. Instead of making theCanadian financial and housing sectors more resilient and sustainable, the outcomes of state responses are best understood as regressively redistributive.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
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".