Harm reduction, medicalisation and decriminalisation
Bibliographic record
Abstract
In this chapter I want to look at harm reduction, medicalisation and decriminalisation, the three reformative, rather than radical features of the debate. These fit more easily into those proposals that soften or mitigate the impact of prohibition. They are less about removing controls, more often about changing direction. Harm reduction The Canadian Centre on Substance Abuse Working Group (1996) outlines five principles of harm reduction, which it says allow drug use to be acknowledged, but not judged, with action to be supportive, not punitive. These principles are: • pragmatism – being realistic and recognising that drug taking carries risk and accepting that abstinence is not necessarily attainable or desirable; • humanistic values , which means respect for the worth and dignity of all persons including drug users; • reducing the negative consequences of drug use , which may not necessarily be promoted by focusing on decreasing or eliminating use; • examining the costs and benefits of drug use in order to arrive at that balance between promoting individual and common good (supervised injection facilities are an example of such a balance); • focusing on and giving priority to the goals listed above , using democratic values of collaboration and participation with those who are marginalised in society. The International Harm Reduction Association, which began in 1996, is more specific. It says that harm reduction refers to policies and programmes that attempt primarily to reduce the adverse health, social and economic consequences of mood-altering substances to individual drug users, their families and their communities (International Harm Reduction Association, 1996, 2008). Taken together, these principles provide an adequate summary of the main features of harm reduction programmes. Most are not contentious, and anyway aimed at such a high level of generality as to be of little direct practical value. Contained within them are suggestions that harm reduction is not concerned with abstinence, nor is there much sympathy with prohibition. Harm reduction involves a recognition that drug abuse is here to stay, at least in the foreseeable future; accordingly, plans must be made to mitigate its evils rather than expect quick-fire solutions. That presumably is also what is meant by ‘pragmatism’, which in this context leads its critics to refer to what they call a ‘poverty of ambition’, and a too-ready acceptance of the existing state of affairs. The International Association makes no distinction between harm to the user and harm to the community.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".