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Record W1546532373 · doi:10.2202/1558-9544.1211

Toxic Choices: The Theory and Impact of Smoking Bans

2011· article· en· W1546532373 on OpenAlex
Ian Irvine, Hai V. Nguyen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForum for Health Economics & Policy · 2011
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of TorontoConcordia University
Fundersnot available
KeywordsQuantile regressionOrder (exchange)EconomicsSmoking banPublic economicsEconometricsBusinessEnvironmental healthMedicineFinance

Abstract

fetched live from OpenAlex

Abstract This paper first proposes a theoretical model of smoker behaviour that serves as a vehicle to evaluate workplace smoking bans. It is a nicotine inventory management model where smoking during one phase of the day impacts utility in other phases. Smoking intensity choice forms part of the optimization. Calibrated model simulations suggest that, with the exception of heavy smokers, workplace bans have small impacts due to substitution possibilities. Quantile regression estimates support the theory. However, restrictions on smoking in the home are an order of greater importance, even when instrumented. The policy conclusion is that workplace ban effectiveness depends heavily upon private choices.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.978

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.299
Teacher spread0.273 · 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