Bibliographic record
Abstract
Abstract: This paper seeks to answer why Insite, Vancouver's safe injection facility, continues to be a controversial issue, given that empirical investigations of the facility have demonstrated that the site improves health and saves health care dollars. As Insite represented a shift to harm reduction policy, from historically dominant drug control policy, it has drawn opposition from local, national and international players. Using a political economy framework, this paper looks at the population around Insite, examining who the clients are and whether the facility is beneficial to them. The Downtown Eastside of Vancouver, where Insite is located, is known to be one of the poorest neighbourhoods in Canada, and as such the population is extremely marginalized (The Globe and Mail, 2008). This paper argues that the attempts by the federal Conservative Party to shut down Insite are based on ideology rather than the effectiveness of the facility. Using the Gramscian concept of hegemony, this paper argues that the federal Conservative Party attempted to use its hegemonic dominance to close down Insite. This paper will also briefly discuss the legal history of Insite, and the empirical literature, briefly summarizing the evidence researchers have found.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".