Bibliographic record
Abstract
Abstract This chapter discusses 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. According to the Canadian Centre on Substance Abuse Working Group, 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. Medicalisation means legally prescribing drugs to users, the user presumably having been assessed by a member of the medical profession, who also decides on the dosage and the drug to be prescribed. Those favouring medicalisation say that the prescribing of drugs to addicts has a number of advantages, one of which is that prescribed drugs do not contain dangerous impurities. Decriminalisation, or depenalisation as it is sometimes called, is often used as synonymous with legalization, since to remove legal controls is also to legalise. Decriminalisation is about assessing legal powers and legal sanctions, and where appropriate reducing or removing them.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".