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Under the influence

2021· article· en· W3158998270 on OpenAlexaff
Raglan Maddox, Pamela M. Ling, Billie-Jo Hardy, Mike Daube

Bibliographic record

VenueTobacco Control · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsBusinessAdvertising

Abstract

fetched live from OpenAlex

The tobacco industry has a long and well-documented history of influencing, exploiting and misleading public health and research communities.1–4 Starting with the 1953 ‘Tobacco Industry Research Committee’5, stakeholders affiliated with the tobacco industry have strategically promoted industry interests through the funding of research programmes and public health initiatives, in order to influence research agendas, manipulate the design, methods and conduct of research, affect interpretation of findings and selectively disseminate information through publications, conferences, forums and panels.1–4 These activities have enabled the tobacco industry to promote its versions of ‘sound science’ and ‘good epidemiology’ which have been designed to weaken consensus about the harms of tobacco use and second-hand smoke exposure.6 Academics and public health communities have sought to raise awareness about and challenge industry interference and manipulation,7–9 and some industry-funded research organisations such as the Council for Tobacco Research and the Center for Indoor Air Research were disbanded as part of the 1998 Master Settlement Agreement in the USA due to their extensively documented role in industry efforts to defraud the public.7 8 However, as Legg et al 10 highlight, the approach has intensified and become more sophisticated over time. A central tenet of the World Health Organization’s Framework Convention on Tobacco Control (FCTC),11 Article 5.3, states: ‘In setting and implementing their public health policies with respect to tobacco control, Parties shall act to protect these policies from commercial and other vested interests of the tobacco industry in accordance with national law’.11 There is now increasing evidence on the ways in which tobacco companies seek to position the industry as a strategic partner to public health, presenting themselves as scientific authorities who are promoting new products as solutions to concerns about commercial tobacco and as legitimate commentators on science and health …

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)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.021
Scholarly communication0.0160.010
Open science0.0020.015
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0690.023

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 head0.020
GPT teacher head0.275
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2021
Admission routes1
Has abstractyes

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