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Record W4384377039 · doi:10.36251/josi82

Taking a leap of faith: Meaningful participation of people with experiences of homelessness in solutions to address homelessness

2015· article· en· W4384377039 on OpenAlexafffundabout
Trudy Norman, Bernie Pauly, H. F. Marks, Dakota Palazzo

Bibliographic record

VenueJournal of Social Inclusion · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of VictoriaCamosun College
FundersMitacs
KeywordsInclusion (mineral)Stigma (botany)FaithSociologyPower (physics)Lived experienceFocus groupPublic relationsPolitical scienceEconomic growthPsychologyGender studiesEconomics

Abstract

fetched live from OpenAlex

Participation of people with experiences of homelessness is critical to the development of meaningful strategies to end homelessness. The purpose of this study was to gain insights from people who have been homeless in a mid-sized Canadian city, as to strategies that facilitate meaningful participation in solutions to end homelessness. Within an overarching framework of collaborative research, we collected data through seven focus groups and employed interpretive description as our approach to data analysis. In our analysis, we identified both exclusionary and inclusionary forces that impact participation. Exclusionary forces included being ‘caught in the homelessness industry’, ‘homelessness is a full time job’ and facing stigma/discrimination that make participation a ‘leap of faith’. Inclusionary forces included earning respect and building trust to address unequal power relations, and restoring often ‘taken for granted’ social relations. Specific strategies to enhance participation include listening, valuing skills and stories, and supporting advocacy efforts. The study findings illuminate ways in which power imbalances are lived out in the daily lives of people who experience homelessness, as well as mitigating forces that provide direction as to strategies for addressing power inequities that seek to make participation and social inclusion meaningful.

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)0.001
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 score0.040
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.100
GPT teacher head0.425
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.

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

Citations4
Published2015
Admission routes3
Has abstractyes

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