Bibliographic record
Abstract
Governments currently classify illicit drugs for various purposes: to guide courts in the sentencing of convicted violators of drug control laws, to prioritize targets of prevention measures and to educate the public about relative risks of the various drugs. It has been proposed that classification should be conducted by scientists and drug experts rather than by politicians, so that it will reflect only accurate factual knowledge of drug effects and risks rather than political biases. Although this is an appealing goal, it is inherently impossible because rank-ordering of the drugs inevitably requires value judgements concerning the different types of harm. Such judgements, even by scientists, depend upon subjective personal criteria and not only upon scientific facts. Moreover, classification that is meant to guide the legal system in controlling dangerous drug use can function only if it is in harmony with the values and sentiments of the public. In some respects, politicians may be better attuned to public attitudes and wishes, and to what policies the public will support, than are scientific experts. The problems inherent in such drug classification are illustrated by the examples of cannabis and of salvinorin A. They raise the question as to whether the classification process really serves any socially beneficial purpose.
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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".