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Record W4402209349 · doi:10.1093/cdj/bsae048

Peer power: how drug user groups navigate harm reduction in Surrey, British Columbia and resist the carceral state

2024· article· en· W4402209349 on OpenAlexaffabout
Colette Michael

Bibliographic record

VenueCommunity Development Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarm reductionPower (physics)State (computer science)HarmCriminologyDrug userPrisonReduction (mathematics)Political scienceSociologyDrugMedia studiesPsychologyLawMedicinePsychiatryComputer sciencePublic healthNursing

Abstract

fetched live from OpenAlex

Abstract The article discusses the Surrey Union of Drug Users (SUDU) in British Columbia, emphasizing harm reduction, safe supply, and destigmatization for drug users. As a peer-led group, SUDU actively resists carceral state policies. The analysis explores challenges faced by peer-led groups, including formation, sustainability, and mobilization efforts. It highlights the need for supportive frameworks to enhance peer-led harm reduction initiatives and argues that such advocacy enables alternative knowledge and institutional innovations that are conducive to building a real world abolitionist future.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.475
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.012
Scholarly communication0.0090.002
Open science0.0020.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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