Street Involved Drug Use, Social Dynamics and Interactions with Police in Ottawa
Bibliographic record
Abstract
Homeless populations are frequently associated with drug consumption.Drug use by homeless people is more visible leading to the assumption that homeless or street involved populations use drugs more frequently or differently than other segments of the population.In this paper, I challenge this idea and consider how homeless and street involved populations consume drugs and how they understand their drug consumption.In 15 semi-structured, openended interviews I explored how homeless and street involved men consume drugs and how they view their drug use.Their drug use is within the broader societal context that impacts their understandings and views of drug consumption.Using Peta Malins' definition of the "junkie", I explore the impact of this idea on how drugs are consumed by homeless and street involved populations.Drawing on the idea of subjectivities, this paper looks at how these individuals understand what it means to be a "junkie" and how they understand their own drug consumption in response.Police have an impact on the daily lives of street involved drug users.This paper explores how police interact with street involved drug users and how street involved drug users understand these interactions.Finally, I consider how the "junkie" subjectivity impacts interactions between street involved users and police.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".