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Record W3211996579 · doi:10.5206/ijoh.2022.1.13647

Navigating and Negotiating Health and Social Services in the Context of Homelessness: Resistance and Resilience

2021· article· en· W3211996579 on OpenAlexaffvenueabout
Sue-Ann MacDonald, Philippe-Benoît Côté

Bibliographic record

VenueInternational Journal on Homelessness · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsEmpowermentHousing FirstSociologyResistance (ecology)Dialogical selfSocial workNegotiationContext (archaeology)Public relationsStigma (botany)Psychological resilienceSocial psychologyPolitical sciencePsychologyMental healthSocial science

Abstract

fetched live from OpenAlex

In this article, we draw upon a case study exploring social inequality and homelessness in homeless-oriented services in a large health and social services centre in Montreal, Quebec, Canada. We take up professionals’ (working in homelessness services) and service users’ (people experiencing homelessness) (N=12) perspectives exploring slippery notions of empowerment/disempowerment using a stigma, resistance, and resilience lens. We mobilize the concepts of navigation and negotiation to better understand participants’ experiences of stigma and the dialogical tensions in empowerment/disempowerment constructs moving beyond simple worker/service user dichotomies of powerful/powerlessness. We explore the more nuanced ways professionals and people who experience homelessness (often described in passive terms) understand these tensions and the dynamics at play within professional relationships, systems, and structural constraints. We unpack professionals’ “silent practices” in their effort to “empower” service users and resist institutional forged by neoliberal pressures. We make the case that systemic and structural constraints manifested in institutional practices push people experiencing homelessness to adopt strategies of resilience.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.407
Teacher spread0.376 · 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 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

Citations5
Published2021
Admission routes3
Has abstractyes

Explore more

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