Bibliographic record
Abstract
All five commentaries raise valuable points that either amplify and reinforce the argument of the paper under discussion [1], or challenge it in a manner that deserves serious consideration. One supportive point is the existence of striking differences in classification of the same drug in different societies as similar ethnically, culturally and scientifically as the Nordic countries [2]. Another is the difference in classification of different forms or different methods of administration of the same drug within a single society, as illustrated by buprenorphine [2], cannabis [3] and cocaine [4]. Such differences arise in part from long-standing traditional practices within a large society [3,5], and in part from transient preferences within small segments of the user population [2,4], but in either case they demonstrate that for control policy purposes, classification on the basis of comparative degrees of harm can not be confined meaningfully to drugs per se, but must also take into account manner of use; amount, frequency and circumstances of use; attributes of different user groups [4]; and prevailing social attitudes and values. Moreover, as stated in the original paper [1] and set out very clearly by Reuter [4], the types and amounts of harm produced by a given drug in a given society can change greatly and even rapidly over time. In view of such complexity, one must agree with Farrell [6] that too much is asked of the classification process. If classification is required for the operation of the judicial system [1,6,7] it is not clear why it must be a classification of drugs rather than of drug-related offences. The definitions of offences could take into account all the complexities mentioned in the preceding paragraph without preventing use of the drugs for therapeutic or research purposes, a goal which Rosenqvist [2] and Ray & Dhawan [3] have noted. Such an approach might even help to reduce the influence of advocacy or pressure groups [3,6] or the sensationalist media [7] in determining policy, as it would be harder to mount a campaign for or against a carefully defined offence than for or against an ill-defined substance. Reuter [4] believes that I consider scientists ‘ill-positioned to make social judgements’; this is somewhat over-stated. I would say, rather, that although scientists in various disciplines can offer factual knowledge of how different policies might or will affect a society, their personal value judgements on what is desirable or undesirable in social policy deserve no more weight than those of other citizens. I agree completely that removal of all drug controls is not a feasible policy [6,7]. The question at issue is what limitations are applied, by whom, and with what rationale. Nutt [7] cites recent public opinion polls to challenge the claim [1] that politicians may be more in tune with public sentiment on drug issues than are scientists. He may well be right, but I believe it is still correct that comparison of the harmfulness of different drugs involves value judgements which the whole public is entitled to make. It is therefore not a bad thing if ‘scientists will have to argue their case at the ballot-box alongside the rest of society’[7]. None
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".