Who Really Gets Higher Cost Home Loans: 2006. Home Loan Disparities By Income, Race and Ethnicity of Borrowers and Neighborhoods in 14 California Communities in 2005
Bibliographic record
Abstract
Homeownership remains the primary path to wealth building for most Californians. With accumulated home equity comes the chance to finance an education, start a business, prepare for retirement, or pass on wealth to children and grandchildren.Higher-cost home loans frustrate this vision. An entire industry has sprung up that offers higher-cost, or subprime, loans to consumers who are thought not to qualify for lowercost prime loans. Higher-cost home loans carry higher interest rates and fees, forcing consumers to pay more to meet often increasing monthly mortgage obligations. Homeowners who face a greater burden in making mortgage payments will have a greater likelihood of falling behind and possibly losing their homes to foreclosure.Consumers who must spend more money on housing costs have less money to meet basic necessities, cover routine home maintenance, and respond to emergencies that may arise. Entire communities suffer when homeowners: have less money to support local businesses, are unable to make needed home repairs that uplift neighborhoods, and lose their homes to foreclosure which can lower neighborhood property values and increase costs to local municipalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".