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Record W2110252975 · doi:10.1177/1090198102251033

State Legislators’ Beliefs About Legislation That Restricts Youth Access to Tobacco Products

2003· article· en· W2110252975 on OpenAlexaff
Nell H. Gottlieb, Adam O. Goldstein, Brian S. Flynn, Joanna E Cohen, Karl E. Bauman, Laura J. Solomon, Michael C. Munger, Greg S. Dana, Laura E. McMorris

Bibliographic record

VenueHealth Education & Behavior · 2003
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsOntario Tobacco Research Unit
Fundersnot available
KeywordsLegislationTobacco controlLegislatureVotingEnforcementTheory of planned behaviorState (computer science)Law enforcementPublic healthPolitical scienceNorm (philosophy)Public administrationPsychologySocial psychologyPublic relationsLawControl (management)PoliticsMedicineEconomics

Abstract

fetched live from OpenAlex

Better understanding of the cognitive framework for decision making among legislators is important for advocacy of health-promoting legislation. In 1994, the authors surveyed state legislators from North Carolina, Texas, and Vermont concerning their beliefs and intentions related to voting for a hypothetical measure to enforce legislation preventing the sale of tobacco to minors, using scales based on the theory of planned behavior. Attitude (importance), subjective norm (whether most people important to you would say you should or should not vote for the law), perceived behavioral control (ability to cast one's vote for the law), and home state were independently and significantly related to intention to vote for the law's enforcement. The results, including descriptive data concerning individual beliefs, suggest specific public health strategies to increase legislative support for passing legislation to restrict youth tobacco sales and, more generally, a framework for studying policy making and advocacy.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.181
GPT teacher head0.461
Teacher spread0.280 · 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 designObservational
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

Citations29
Published2003
Admission routes1
Has abstractyes

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