What the History of Drugs Can Teach Us About the Current Cannabis Legalization Process: Unfinished Business
Bibliographic record
Abstract
Over time, there have been considerable changes in the variety, availability, production, distribution, and use and user(s) of psychoactive substances, the meaning of substance use and its impact on users and their social or physical environment(s). This article reviews the mechanisms of introduction of psychoactive substances such as alcohol, tobacco, coffee, tea and cannabis to populations and communities that did not have them before. It considers the historical tension between early adopters who greet new substances with various levels of enthusiasm in their eagerness to enjoy what they believe to be the benefits of using these substances, and those focused on what they believe to be the negative aspects of use, who decry these new substances with horror. With more nonusers than users in the population, social policies tend to be directed at preventing, restricting, or punishing selected use, users and .drugs., using controls and interventions such regulation, incarceration, death sentence, treatment, prevention, legalization, taxation, among others. Whatever their intent or wished-for impact, all had consequences that produced additional, unplanned for, and (often) negative effects. This paper will consider some of these sequences as they occurred historically with other substances in light of the current shift to legalization and normalization of cannabis, noting the mechanisms of use, controls, and consequences of some types of formal interventions and give some attention to how and what we can learn from our experiences in order to plan ahead and become better prepared to successfully deal with the 'unexpecteds' of that well-known 'hell' paved with good intentions.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.051 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".