More than Spare Change: A Case Study of Contact and Voter Support for the Homeless in Los Angeles County, California
Bibliographic record
Abstract
Research on local welfare spending for individuals and families experiencing homelessness has characterized such spending as severely limited and constrained. Studies on fiscal federalism have argued that competition between local governments prevents leaders from spending much, if anything, to assist the homeless. County governments may be further constrained from providing assistance by state constitutions or statutes. Finally, local businesses may organize and lobby for punitive, rather than ameliorative, local treatment of the homeless. In this study, we argue that ordinary voters matter for local spending for the homeless, particularly in states where they are empowered to affect local government spending through ballot propositions. Accordingly, we mobilize data from a ballot initiative in Los Angeles County and present an exploratory study of the determinants of voter support for homelessness relief. We find that range of factors, particularly partisanship and contact with the homeless, strongly predict support for spending on the homeless.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".