Dangerous Opportunities : The Future of Financial Institutions, Housing Policy, and Governance
Bibliographic record
Abstract
The 2017 Home Capital saga represents the shortcomings of a financial system challenged by distinct, siloed regulatory frameworks that fail to communicate with each other. Home Capital is a publicly traded company that acts as a lender through the Home Trust Company, most often providing mortgages to clients rejected by traditional banks. Home Capital's 2017 announcement that it required $2 billion to sustain a $600-million loss shook customer confidence, and fueled by allegations of corruption, the company suffered a rapid decline in stock price. The Home Capital crisis is the most recent pre-pandemic example of systemic risk in the financial sector in Canada and highlights the invaluable opportunity we have to avoid repeating past mistakes in the nearing post-pandemic economic reality. Using the 2017 Home Capital saga as a starting point, Dangerous Opportunitiessheds light on the compartmentalization of regulators and its greater ramifications on board independence and corporate governance, taxation in the competitive housing sector, and the success of non-bank financial institutions in various jurisdictions. A hybrid of law and business, Dangerous Opportunitiesis a must-read for those interested in the underbelly of financial institutions and is an inspired read in the aftermath of the recent housing crisis, which saw many aspiring homeowners seek dangerous opportunities outside of the traditional banking system.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".