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Record W4404471197 · doi:10.1093/bjsw/bcae183

Challenging charitable oppression: Social working in supportive housing

2024· article· en· W4404471197 on OpenAlexaffabout
Nicole Laurenne Browne

Bibliographic record

VenueThe British Journal of Social Work · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOppressionSociologyBusinessPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Abstract This article provides an autoethnographic analysis of my experiences of administrating and supporting transitional housing programs in Canada. Using anti-colonial, critical race feminist, critical disability studies, and mad studies framings, the article aims to highlight the sharp contrast between social work values and social care practice in housing work. I direct my gaze as a researcher toward myself as a social worker, exploring my experience social working in practice. A creative non-fiction approach to storytelling is used based directly on my experiences social working in supportive housing. These creative non-fiction pieces were analysed to draw out relevant themes. Themes include the discourse of dangerousness, challenging charitable oppression, and reproducing colonial carceral structures. The narratives are followed by analyses which demonstrate harms perpetuated in the neoliberalist social care landscape in Canada. My analysis is based on anti-colonial, critical race feminist, critical disability studies, and mad studies framings. Challenges discussed include interdisciplinary social care practice with those experiencing significant barriers to housing and the ethical implications of working under a charitable organization, along with suggestions for countering dominant ableist, sanist, and Eurocentric practice.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.068
GPT teacher head0.394
Teacher spread0.326 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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