Bibliographic record
Abstract
The formulation of policy on cocaine, as on any other social issue, involves explicit or implicit cost-benefit analyses with many factors. Cocaine use carries many medical, psychiatric and social risks, and its inherent pharmacological risk of dependence is greater than for other drugs. The reported frequency of these problems has increased exponentially over the past fifteen years. However, current levels of use are decreasing in the general population, though still increasing among certain subpopulations in which it is accompanied by violent crime. On the other hand, the attempt to control use mainly or exclusively by reducing the supply has been of low efficacy and extremely expensive, in both human and monetary terms, for the consuming countries and economically and politically devastating for the producing countries. Yet past experience with other drugs suggests that legalization of cocaine would increase its use substantially. Moreover, legalization runs counter to public sentiment, even in those countries where the law is applied leniently against users and small-scale traffickers. The most practical policy appears to be to maintain prohibition as a sign of social disapproval, but to rely much more heavily on non-coercive measures to reduce demand by strengthening public consensus against all drug use.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".