Pleasurable substances and regulation: old wine in new rules?
Bibliographic record
Abstract
In contrast with the critical, innovative ideas developed between the 1960s and the 1980s regarding the way we deal with illegal drugs in our societies, the current dominant approaches frame the issue of drugs as a matter of crime, public order, and control. Pleasurable substances have always existed and always will, and so the efforts to cope with them. However, we witness today remarkable developments at local, national and international levels in the fields of drug policies (on cannabis for example), drug trafficking (new routes, new actors) and drug use (new substances, new drug cultures), all of which deserve our attention and push us to think beyond the repressive paradigm. This contribution, which also serves as an introduction for this special issue of ToCC on drugs, aims to present an overview of the main developments taking place, and challenges ahead, within the three above-mentioned fields. There are new markets and trends in the use of legal and illegal pleasurable substances, particularly regarding synthetic drugs (amphetamines, methamphetamines and new psychoactive substances or NPS), tobacco and alcohol. Illegal drugs are supplied from changing countries and through new routes, while retailing increasingly takes place through the so-called cryptomarkets (online). Effective policies are rendered impossible by the fundamental repression paradox: the more intensive and effective the repression, the larger the profits of drug traffickers and the balloon effects (displacement). Despite the harms and negative effects of repressive policies have extensively been documented, a societal debate towards the regulation of illegal drugs is hindered by the use of false dichotomies or presuppositions, by the use of ethical or moral appeals, or by lack of political will. Also the debate in the media is static, superficial and full of clichés. Scientific research on drugs also follows specific agendas and it is focussed on particular aspects of the problem. Changes to end the ‘war on drugs’, certainly regarding cannabis, are however underway in many places at local and national level (Uruguay, Canada, US, Spain, etc.), this despite UN bureaucracies and international conventions that fiercely resist those changes.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".