Commentary: Harm reduction, managed alcohol programs and doing the right thing
Bibliographic record
Abstract
In this commentary to the special issue on managed alcohol programs, the necessity of working from a harm reduction orientation when supporting people who experience homelessness is explored. While not all people who experience homelessness have substance use disorders, many respond to experiences of trauma and exclusion through the use of substances, and in many cases this leads to problematic use. In a context where people who are homeless regularly experience the control and regulation of their lives through emergency services, harm reduction approaches provide a welcomed alternative through humane, respectful, effective and client centred approaches to addressing substance use disorders. The articles in this volume demonstrate the value of managed alcohol programs to support people whose consumption of alcohol is problematic. The emerging evidence base for managed alcohol programs has important implications for policy and practice.
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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.054 | 0.041 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".