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Record W1996784546 · doi:10.1177/0020715206070267

Global Patterns and Determinants of Sex Differences in Smoking

2006· article· en· W1996784546 on OpenAlexvenueno aff
Fred C. Pampel

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsDemographyCigarette smokingGender equalitySame sexTobacco controlTobacco usePsychologyMedicinePublic healthSociologyGender studiesDevelopmental psychologyPopulation

Abstract

fetched live from OpenAlex

The worldwide spread of tobacco use in recent decades raises questions about the relative prevalence of smoking among men and women. Does the degree of gender equality in nations promote equality in cigarette use? Does rising use of cigarettes by women stem from the stage of cigarette diffusion and earlier increases among men? Or have changes in economic factors and smoking policy affected the sexes differently? This study uses aggregate data for 106 nations, measures of smoking prevalence circa 2000, and lagged measures of gender equality, cigarette diffusion, and tobacco access to address these questions and evaluate the underlying theories. With the logged ratio of female to male prevalence as the dependent variable, regression results reveal that gender equality has inconsistent effects on women's smoking relative to men, cigarette diffusion has more consistent and moderately strong effects, and economic factors have weak effects. Global patterns of adoption of cigarettes by women appear most closely associated with the early adoption by men and then movement through a regular pattern of cigarette diffusion.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.398
Teacher spread0.320 · 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

Citations113
Published2006
Admission routes1
Has abstractyes

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