MétaCan
Menu
Back to cohort
Record W4407275633 · doi:10.1080/09687637.2025.2461006

The social organization of structural vulnerability among people who are homeless and use drugs: an institutional ethnography

2025· article· en· W4407275633 on OpenAlexafffundabout
Naomi Nichols, Samantha Blondeau

Bibliographic record

VenueDrugs Education Prevention and Policy · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnographyVulnerability (computing)Social vulnerabilitySociologyPsychologyCriminologySocial psychologyComputer securityAnthropology

Abstract

fetched live from OpenAlex

Background In this article, we describe an institutional ethnographic investigation of the housing and homelessness response in one municipality in Ontario, Canada.Methods Drawing on 42 interviews with social service professionals and municipal government employees and 49 interviews with people who are or have been using municipal housing and homeless services (e.g. emergency shelters, mobile healthcare, outreach services, transitional housing), we bring into view some of the inter-institutional and organizational processes that shape housing precarity among people who use drugs.Results Our research pinpoints specific institutionally-organized processes – i.e. structural vulnerabilities – that expose people who use drugs to housing loss, unsheltered homelessness, and difficulties in re-housing (or homeless chronicity). In many cases, the processes also make people more vulnerable to harms associated with substance use.Conclusion The specificity of our analysis points toward policy, legislative and institutional reforms that could improve access to justice, health, and housing for people who use drugs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.503
Threshold uncertainty score0.999

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.014
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.379
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueDrugs Education Prevention and PolicySame topicHIV, Drug Use, Sexual RiskFrench-language works237,207