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Record W4323928494 · doi:10.5206/ijoh.2022.2.14785

Community Acceptance of, and Opposition to, Homeless-Serving Facilities

2023· article· en· W4323928494 on OpenAlexvenueno aff
Brian E. Adams, Megan Welsh, Nicolás Gutiérrez

Bibliographic record

VenueInternational Journal on Homelessness · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsRespondentThematic analysisFocus groupPaternalismMental healthPublic relationsPsychologyOpposition (politics)Substance abuseNursingSocial psychologySociologyPolitical scienceMedicineBusinessQualitative researchPsychiatryPoliticsMarketing

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.441
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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