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Record W2143206499

Profiling the street-level drug trafficker on Vancouver's Downtown Eastside

2005· dissertation· en· W2143206499 on OpenAlexaboutno aff
Kashmir Heed

Bibliographic record

VenueSummit (Simon Fraser University) · 2005
Typedissertation
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownProfiling (computer programming)DrugDrug traffickingExploratory analysisExploratory researchDrug controlCriminologyAdvertisingPolitical scienceGeographyPsychologySociologyPsychiatryBusinessSocial scienceData scienceArchaeologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

The primary objective of this thesis is to conduct exploratory research to examine the possible variables that lead to the perpetuation of the drug problem, in the hope that such findings may suggest more effective approaches to solving the drug problem. The literature relating to the "war on drugs" is examined, and the data relating to 600 streetlevel drug traffickers arrested in Vancouver, BC between June 2001 and October 2002 are analyzed to form an empirically based profile of the drug trafficker in Vancouver's Downtown Eastside. Among other things, the findings reveal that a small group of "professional criminals" specialize in the trade of drug trafficking in the Downtown Eastside. The author argues that a greater understanding of the nature and characteristics of the retail drug trafficker and trade is needed for successful future policy on drug regulation and control.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.691

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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
Published2005
Admission routes1
Has abstractyes

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