Community Acceptance of, and Opposition to, Homeless-Serving Facilities
Bibliographic record
Abstract
Under what conditions will the public accept homeless-serving housing and social service facilities in their neighborhood? In this paper, we answer this question through a basic descriptive statistical analysis of a brief survey (respondent n=251) and a thematic analysis of seven focus groups with residents of San Diego, California (participant n=34). We find that although residents were not categorically opposed to such facilities, their support was contingent on a particular approach to addressing homelessness, often rooted in misperceptions of the causes of homelessness. Participants classified people experiencing homelessness (PEH) into “deserving” and “undeserving” groups based on these perceptions. Attitudes towards homeless-serving facilities were also shaped by a belief that what is needed most are services such as substance abuse treatment, mental health services, or job training; they focused less on the need to house people who are currently unsheltered. Study participants also took a paternalistic approach to policy design, focusing on rules and regulations to force PEH to make “good” decisions. Participants recognized homelessness as a pressing social problem and were willing to consider homeless-serving facilities in their community. However, their attitudes and beliefs limited which facilities they would support, and under what circumstances.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".