MétaCan
Menu
Back to cohort
Record W3133741172 · doi:10.1002/pam.22485

The effect of e‐cigarette taxes on pre‐pregnancy and prenatal smoking

2023· article· en· W3133741172 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Policy Analysis and Management · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Institute on Drug AbuseHealth CanadaCenters for Disease Control and PreventionNational Institutes of HealthTulane UniversitySan Diego State UniversityGeorge Mason University
KeywordsCigarette smokingPregnancyMedicineLegislationEnvironmental healthPrenatal careObstetricsEconomicsDemographyPolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

E-cigarette taxes are an active area of legislation and have important regulatory implications by proxying e-cigarette accessibility. We examine the effect of e-cigarette taxes on prepregnancy and prenatal smoking using the near-universe of births to mothers conceiving between 2013 and 2019 in the United States. Using fixed effect regressions, we show that e-cigarette taxes increase prepregnancy and prenatal smoking. We also find evidence that e-cigarette taxes reduce prepregnancy and 3rd trimester e-cigarette use. Finally, we show that e-cigarette taxes increase news coverage of e-cigarettes and raise perceptions of risk of e-cigarettes.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.316
Teacher spread0.303 · 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