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Record W3212423571 · doi:10.11575/prism/39271

Supervised Consumption Sites in Canadian Neighbourhoods: The Role that Physical Design and Location Play in Community Relations

2021· dissertation· en· W3212423571 on OpenAlexaboutno aff
Erik Mohns

Bibliographic record

VenueOpen MIND · 2021
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)GeographyRegional scienceEnvironmental planningSociologySocial science

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.307
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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