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Record W7010221815

Harm reduction in policing: responding to persons under the influence of illicit drugs

2008· article· en· W7010221815 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCollembola Taxonomy and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHarm reductionIllicit drugPublic healthNeighbourhood (mathematics)DowntownHarmDrugPoison control
DOInot available

Abstract

fetched live from OpenAlex

The Public Health Agency of Canada estimates between 75,000 and 125,000 injection drug users are addicted to drugs that include heroin, cocaine or amphetamines. Over fifteen thousand drug users are estimated to reside in the Greater Vancouver area; 69% have reported sharing needles. The Greater Vancouver region, with special consideration for the Downtown Vancouver Eastside, Canada's poorest neighbourhood and the epicentre for injection drug use (IDU), has a high rate not only of illicit drug use but also illicit drug possession and trafficking. It is estimated that nearly half of Vancouver's IDUs ( 4, 700 ID Us and 1,000 street youth) reside in this area covering approximately ten city blocks. On a day-to-to day basis, police officers routinely interact with individuals that are under the influence of illicit drugs placing themselves at risk not only of potential violent confrontation but also of inadvertently exposing themselves to communicable diseases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.187
Teacher spread0.177 · 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 designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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