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Record W4378902509 · doi:10.3390/psychoactives2020013

Supervenience and the Public Health Standard for Psychoactive Substances

2023· article· en· W4378902509 on OpenAlexaff
Michael Chaiton

Bibliographic record

VenuePsychoactives · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Tobacco Research UnitUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsCausationSupervenienceHarmPublic healthEpistemologySociologyPositive economicsPolitical scienceSocial psychologyPsychologyMedicinePhilosophyEconomicsMetaphysics

Abstract

fetched live from OpenAlex

This paper uses Geoffrey Rose’s theory of population health change to identify that part of lack of resolution of the harm reduction debates emerges from the philosophical question of the possibility of group-level causation. Group-level causation, otherwise known as the supervenience of higher levels onto lower levels, has been a core matter of philosophical and ethical debate. This paper argues that there, the public health standard depends crucially on the contextual environment rather than the individual product’s intrinsic properties. Interpretation of the benefits or the harms of e-cigarettes or other harm reduction products depends on the often unstated assumptions of the potential policy and cultural environments in which e-cigarettes exist, as well as the separate debate over the absolute or relative level of harm of e-cigarettes. Unfortunately, most of the research conducted will not contribute to our understanding of the potential impact of e-cigarettes until we focus on the potential changes to the social and political environment.

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 imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0040.048
Scholarly communication0.0070.010
Open science0.0030.007
Research integrity0.0160.018
Insufficient payload (model declined to judge)0.0030.001

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 head0.088
GPT teacher head0.394
Teacher spread0.306 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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