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Record W2898955617 · doi:10.1111/ijsw.12353

When political values and perceptions of deservingness collide: Evaluating public support for homelessness investments in Canada

2018· article· en· W2898955617 on OpenAlexaffabout
Carey Doberstein, Alison K. Smith

Bibliographic record

VenueInternational Journal of Social Welfare · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsIdeologyVignettePoliticsPerceptionWelfareBeneficiaryGovernment (linguistics)Welfare statePolitical scienceSocial psychologyPeacetimePublic economicsPsychologyEconomicsLaw

Abstract

fetched live from OpenAlex

Citizen attitudes toward welfare state investments are often explained by their ideological values and their perceptions of deservingness of welfare recipients, yet recent experimental research has led to the theorization that clear deservingness cues can overwhelm otherwise strong ideological beliefs. We tested these claims with respect to homelessness in Canada using a vignette survey experiment and found evidence that citizens with very different political beliefs can support similar government investments, indeed from a shared sense of deservingness as suggested by recent experimental studies, but that support is anchored by rather different reasons. Key Practitioner Message: • Citizen support for homelessness investments is jointly mediated by ideology and a sense of the “deservingness” of the beneficiary. • Emphasizing the broader cost savings to taxpayers from “Housing First” does not make conservative‐leaning citizens more supportive of investments. • Emphasizing the personal attributes of persons experiencing homelessness rather than abstract statistics may unite progressives and conservatives on “deservingness”.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.444
Teacher spread0.374 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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