The case for safe injection sites: examining 'harm reduction' in insite`s communication strategies
Bibliographic record
Abstract
The term “harm reduction” has been used as a label for certain policies and programs in the field of illicit drugs for many years, but there has never been a universal definition for the term or unanimous consensus on how the term should be used. Some proponents argue that harm reduction must be a movement that challenges traditional drug laws, while others believe that harm reduction should chiefly be a public health approach that aims to improve the overall health of drug users. Some scholars hail harm reduction for taking an amoral and value-neutral position towards drug use, while others criticize it for devaluing human rights and perpetuating the marginalization of drug users. Drawing on Foucault’s framework of governmentality, Petersen and Lupton’s (1996) concept of the “new public health,” and Goffman’s (1963) theories on stigma, this research investigates the types of claims and arguments that InSite—Canada’s only supervised injection site and perhaps its most recognized harm reduction program—uses in its website and press releases to characterize and justify its services. Three news articles from The Vancouver Sun are also examined for a comparison of the complexities and diverse viewpoints that often arise in descriptions and defenses of harm reduction, supervised injection service, and illicit drug use.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".