Burning Down the House: Law, Emotion, and the Subprime Mortgage Crisis
Bibliographic record
Abstract
This paper contends that an important but under-explored cause of the United States’ subprime mortgage crisis is contract law’s derisory response to emotion and the defective risk allocation model that it helped to generate. Contract law cannot deal proactively or fairly with emotions – including irrational public enthusiasm and the deep desire for home ownership – because it views the world through the steely eyes of the reasonable man. As a countermand, the law must work to stave off disastrous subprime loans, especially for the honest, first-time buyer, by acknowledging that housing contracts have a concentrated emotional overlay. The paper examines legislative reform to date, which has made some progress in re-establishing the financial link between originating lender and borrower (which securitization has recently disrupted) as well as in requiring enhanced and meaningful mortgagee disclosure in order to ameliorate contract law’s inability to address the emotional component of many mortgage transactions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".