Supervised Consumption Sites in Canadian Neighbourhoods: The Role that Physical Design and Location Play in Community Relations
Bibliographic record
Abstract
Across Canada, 6,214 overdose fatalities occurred in 2020, with 21,174 overdose deaths recorded from January 2016 to December 2020 (Public Health Agency of Canada, 2021, p5). With the ongoing opioid crisis, supervised consumption sites (SCSs) are becoming permanent fixtures in many Canadian cities. Similarly, we are coming to understand the importance of built forms and their relationship to behaviors in everyday life. Many community members are opposed to having SCSs placed in their communities as they link them to an increase in social disorder, leading to more crime (Wallace, Chamberlain, Fahmy, 2019; Sampson & Raudenbush, 1999). However, this contradicts the literature on SCSs (Wood et al., 2006). In exploring the relationships between built forms of SCSs and their surrounding communities, I found that SCSs do not directly contribute to social disorder. Instead, social disorder in these locations predates the implementation of SCSs. The built forms of SCSs are at a unique intersection of space and public health. SCSs provide a life-saving service through harm reduction practices, but they go beyond this initial purpose and take on new meanings and purposes for those in the community. While those meanings differ SCSs remain an important part of community growth and are essential to healthy urban development. Simply ignoring addiction, poverty, and mental health issues during development/redevelopment in communities places the burden of these issues unfairly on businesses and community members. This results in further stigma and conflict in public spaces.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".