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Record W2115278257 · doi:10.5130/ijcre.v8i1.3936

Shifting the evaluative gaze: Community-based program evaluation in the homeless sector

2015· article· en· W2115278257 on OpenAlexaff
Bruce Wallace, Bernie Pauly, Kathleen Perkin, Mike Ranfft

Bibliographic record

VenueGateways International Journal of Community Research and Engagement · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeneral partnershipParticipatory action researchPublic relationsCommunity developmentParticipatory evaluationCitizen journalismCommunity-based participatory researchSociologyHousing FirstEconomic growthPolitical sciencePsychologyMental healthSocial science

Abstract

fetched live from OpenAlex

Homelessness is a growing social issue that is a consequence of structural inequities and contributor to the development of health inequities. Community-based research (CBR) has been proposed as an effective research strategy for addressing health equities and promoting social justice through participatory processes. The purpose of this article is to examine the application of CBR principles and practices in the homeless sector and the implications for the production of knowledge and social change to address homelessness. Drawing on our experiences as researchers and service providers, we reflect on the significant successes and challenges associated with using CBR in the homelessness sector. In our discussion we emphasise insights, challenges and lessons learned from a community-university partnership that focused on an evaluation of a transitional shelter program in a large urban centre where housing is expensive and often unavailable.Keywords: Homelessness, housing, transitional housing, transitional shelter, program evaluation, community-based research

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)0.126
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1260.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.008
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 head0.673
GPT teacher head0.614
Teacher spread0.059 · 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.

Study designQualitative
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

Citations6
Published2015
Admission routes1
Has abstractyes

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