MétaCan
Menu
Back to cohort

REPLY TO THE COMMENTARIES

2010· letter· en· W1896918087 on OpenAlexaff
H. Kalant

Bibliographic record

VenueAddiction · 2010
Typeletter
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsCanada Research Chairs
Fundersnot available
KeywordsHarmArgument (complex analysis)Set (abstract data type)PopulationCannabisProcess (computing)Point (geometry)AddictionControl (management)PsychologyMedicinePositive economicsSociologySocial psychologyPsychiatryComputer scienceArtificial intelligenceMathematicsEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.029
GPT teacher head0.313
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAddictionSame topicPsychedelics and Drug StudiesFrench-language works237,207