Bibliographic record
Abstract
Abstract Medically supervised injecting rooms (MSIRs) are legally approved facilities whereby illicit drug users can inject substances such as heroin in relative safety under the supervision of medically trained personnel. More than 100 MSIRs have been introduced in 60 cities internationally based on evidence that they are an effective harm reduction method for preventing overdose-related injury and death. But their introduction in Australia has been subject to ongoing political and ideological contention. The first Australian MSIR was established in Kings Cross, Sydney, in 2001 and made permanent in 2011 following a number of positive evaluations. The Victorian path has been less smooth. The Victorian state Labor government proposed the introduction of five MSIRs across varied suburbs in 2000, but their introduction was blocked by a conservative majority in the Upper House of Parliament. Despite ongoing campaigns for MSIRs in areas of high heroin-related overdoses by a coalition of professionals, politicians, and local governments and residents, it was not until 2018 that a state Labor government introduced an MSIR in the City of Yarra. Based on my 25 years of involvement in MSIR research, this chapter critically explores the key arguments around the merits and limitations of an MSIR in Victoria. Attention is drawn to ideological divisions around prohibition versus zero tolerance, the influential role of the media, the impact of research evidence, the divisions among local residents, and the competing views of the major political parties. Some conclusions are drawn about the factors which finally enabled the introduction of an MSIR despite continuing public discord.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".