The Changing Landscape of Affordable Housing in the Rural and Urban United States, 1990–2016*
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
Abstract Affordable housing has declined in recent decades, yet limited research has examined the demographic and economic changes influencing place‐level affordability—especially outside of large metros. In this study I examine the effects of county‐level population growth and decline, population aging, and natural amenity development on rates of affordable housing, income, and housing costs across four types of counties. While declines in affordability from 1990 to 2016 were universal between rural and urban counties, population growth is associated with decreases in affordability in rural counties but increased affordability in large metros counties due to estimated decreases in housing costs. Population aging is estimated to improve affordability in large and small metro counties, despite the associated decrease in income and housing costs across all county types. The effects of aging vary greatly between owners and renters. Natural amenity development, despite its theoretical importance, is not associated with changes in affordability for rural counties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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