Bibliographic record
Abstract
Canada’s implementation of Supervised Consumption Sites remains controversial, despite a growing opioid overdose mortality crisis. In 2019, the Alberta United Conservative Government published in affiliation with Alberta Health, ‘Impact: A Socio-Economic Review of Supervised Consumption Sites in Alberta’. Following publication, the review became an important referent document used by governments to prevent supervised consumption sites from operating; as Alberta’s overdose deaths increased, the provincial government froze supervised consumption site funding, shutting down North America’s busiest sites. These events indicate the need to analyze how supervised consumption sites and harm reduction is now communicated by the Alberta Government, with Alberta Health. This cross-sectional case study asks: what discourse is produced in Alberta Health’s ‘Socio-Economic Review of Supervised Consumption Sites in Alberta?’ The methodology is informed by Van Dijk’s Critical Discourse Analysis and Michel Foucault’s concepts of knowledge and power. The two major themes identified, site inefficiency and risk to society, evidence a neoliberal governmental discourse on health services. Findings indicate that neoliberalism silences the voices of site users and social issues to emphasize the negative community impact of supervised consumption sites. Consequently, the review’s neoliberal governmental discourse repositions the fundamental problem underlying drug addiction away from the silenced, systemic, socio-economic marginality site users face to the salient, socio-economic challenges that harm reduction sites impart on the community. This discourse erodes health and social services like harm reduction to rationalize the Alberta Government's newest addiction treatment proposal, the forced treatment model, increasing disciplinary measures against society’s most vulnerable.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".