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Record W4210440430 · doi:10.1016/j.ssmqr.2022.100055

Experiences with compounding surveillance and social control as a barrier to safe consumption service access

2022· article· en· W4210440430 on OpenAlexafffundabout
Carolyn Greene, Marta‐Marika Urbanik, Rachel Geldart

Bibliographic record

VenueSSM - Qualitative Research in Health · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of AlbertaAthabasca University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarm reductionConsumption (sociology)BusinessService (business)Public relationsHarmAccess controlPublic healthControl (management)Internet privacyService providerSocial workComputer securityMarketingMedicinePolitical scienceSociologyEconomic growthNursingPsychologyComputer scienceEconomicsSocial psychology

Abstract

fetched live from OpenAlex

Barriers to accessing supervised consumption services are well documented in the literature. Police and security presence in the areas surrounding these sites are two such barriers. Yet, despite what we know about these autonomous social control actors, less is known about whether/how the convergence of actors within neighborhoods housing supervised consumption sites shapes service access. This paper examines how people who use drugs navigate police, security, and residents to access harm reduction services in Calgary, Canada. Based on qualitative interviews with persons who use drugs, our findings suggest that these public health services are undermined when police, security and area residents converge in their efforts to address social ‘disorder’. Participants reported displacement from the area surrounding these health services, resulting in greater public drug use and reduced service access. This research extends knowledge on supervised consumption site access barriers and social control of 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 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)0.014
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 score0.029
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.381
GPT teacher head0.614
Teacher spread0.233 · 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

Citations22
Published2022
Admission routes3
Has abstractyes

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