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Record W7162337846 · doi:10.7202/1123962ar

“As it happens”: Co-creating knowledge for a gender-based safety audit with women experiencing long-term homelessness

2025· article· fr· W7162337846 on OpenAlexvenueaboutno aff
Mary-Elizabeth Vaccaro, Stephanie Milliken, Yaungtinec Samana, Samm Floren

Bibliographic record

VenueCanadian social work review · 2025
Typearticle
Languagefr
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsForegroundingReflexivityAuditScholarshipTransformative learningSocial workWork (physics)IntersectionalityFeminism

Abstract

fetched live from OpenAlex

In this paper, we explore an intersectional feminist approach to co-creating knowledge on safety with women and gender-diverse people experiencing long-term, street-based homelessness in Hamilton, Ontario. Using a gender-based safety audit (GBSA) framework, our project team of front-line social service providers, academic researchers, and community co-leads collaborated to engage participants in arts-based workshops to co-produce a zine focused on the community’s perceptions of safety and recommendations for action. Drawing from our own experiences as well as existing literature on the lived realities of women and gender-diverse people experiencing homelessness, our paper critically reflects on the barriers and ethical considerations inherent in knowledge co-creation projects with highly marginalized communities. By foregrounding the perspectives and recommendations of those directly affected by gender-based homelessness, our work also contributes to scholarship on feminist knowledge co-creation methodologies. Through reflexive dialogues within our project team, we highlight the transformative potential of arts-based knowledge co-creation projects that engage marginalized communities in advocating for social change.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.436
Teacher spread0.358 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes2
Has abstractyes

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