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Record W2900562455 · doi:10.1111/joca.12232

Integrating Negative Social Cues in Tobacco Packaging: A Novel Approach to Discouraging Smokers

2018· article· en· W2900562455 on OpenAlexfundno aff
Jennifer Jeffrey, Matthew Thomson

Bibliographic record

VenueJournal of Consumer Affairs · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingPsychologySocial psychologyIdentity (music)Tobacco controlSocial identity theoryConsciousnessFear appealAdvertisingPublic healthMedicineSocial groupBusinessAesthetics

Abstract

fetched live from OpenAlex

Smoking is an international health crisis. Tobacco packaging is an important vehicle to convey antismoking messages, which to date have been predominantly limited to fear‐based health appeals. Using an experimental approach, we examine whether a novel alternative—using negative social cues on packaging—is effective at discouraging smoking. Our results support the notion that packaging which conveys to smokers that “others” view smoking negatively is sufficient to trigger feelings of self‐consciousness, which in turn reduces smoking intentions. This approach is particularly effective in “isolated” smokers who do not see smoking as identity‐relevant or congruent with their social self. These findings suggest that for a particular segment of the smoking population, the integration of negative social cues on packaging may be an effective complement to current fear‐based appeals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.398
Teacher spread0.324 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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