The Relationship Between Exits From Federally Subsidized Housing and Wages, King County, WA
Bibliographic record
Abstract
Federally subsidized housing programs aim for economic self-sufficiency. We modeled housing exit type’s relationship with wage income using public housing authority exit data and Washington State wage data. Our cohort included 1,974 exits. Positive exits had higher mean wages ($8,392 vs. $6,643 and $6,253) and working hours (432 vs. 373 and 355) compared to neutral and negative exits, respectively. Households with positive exits were more likely to earn a living wage (33.5%) than those with neutral (16.9%) or negative (15.1%) exits. According to our model, positive exits earned an additional $1,593 (95% confidence interval: $1,031, $2,156) per quarter compared to negative exits. Wages among positive exits were substantially higher than those among neutral exits for four quarters before and after exit; wages among neutral exits were slightly higher than those for negative exits. These methods can assess the impact of programs targeting economic self-sufficiency among housing support recipients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".