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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it