Book Note: Other People’s Houses: How Decades Of Bailouts, Captive Regulators, And Toxic Bankers Made Home Mortgages A Thrilling Business, by Jennifer Taub
Bibliographic record
Abstract
OTHER PEOPLE’S HOUSES ADDS TO A GROWING LITERATURE on the origins of the Financial Crisis of 2008. Jennifer Taub’s contribution provides one of the most comprehensive studies to date. She outlines the many causes of the crisis, dispels myths, and explains what has been done—or how little has been done—in the wake of the crisis, while keeping the perspectives of struggling homeowners front and center. Taub begins by showing how the 2008 crisis was a continuation of the savings and loans crisis of the 1980s and 1990s that brought countless farmers and banks to ruin. In both crises, the “same players” operated, she argues, just under “new names” and in “new institutions with the same frailties.”2 She also reveals how the United States Congress, under pressure from the saving and loans industry, dismantled the New Deal regulatory framework, thus allowing savings and loans institutions to operate with much more risk. This deregulation and “desupervision” of the industry would become an essential cause of the 2008 crisis.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".