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Record W2065728871 · doi:10.2747/0272-3638.33.8.1144

"A Little Heaven in Hell": The Role of a Supervised Injection Facility in Transforming Place

2012· article· en· W2065728871 on OpenAlexaffabout
Ehsan Jozaghi

Bibliographic record

VenueUrban Geography · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDowntownTransformative learningHeavenNarrativeQualitative researchSample (material)SociologyPublic relationsMedicinePolitical scienceGeographySocial science

Abstract

fetched live from OpenAlex

While numerous studies on InSite (North America's first supervised injection facility) have been published in leading international journals, little attention has been given to the facility's role in the local culture of drug use and its transformation of place in Vancouver's Downtown Eastside. This study analyzes the transformative role of InSite in the lives of injection drug users (IDUs). Semi-structured qualitative interviews were conducted with a small, purposively chosen sample of IDUs attending InSite. Interviews were transcribed verbatim and analyzed thematically using NVivo 8 software. Participants' narratives indicate that attending InSite has had numerous positive effects in their lives, including changes in sharing behavior, improving health, establishing social support and saving their lives. Furthermore, attending InSite has been particularly effective in creating a unique microenvironment where IDUs are increasingly identifying the facility as their community center.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.089
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.018
Scholarly communication0.0060.003
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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