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Record W2176501827 · doi:10.1509/jmkr.48.5.799

Fast-Food Consumption and the Ban on Advertising Targeting Children: The Quebec Experience

2011· article· en· W2176501827 on OpenAlexaffabout
Tirtha Dhar, Kathy Baylis

Bibliographic record

VenueJournal of Marketing Research · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConsumption (sociology)Unhealthy foodChildhood obesityFood consumptionAdvertisingEnvironmental healthObesityConsumer Expenditure SurveyBusinessEconomicsAgricultural economicsMedicinePublic economicsOverweight

Abstract

fetched live from OpenAlex

Amid growing concerns about childhood obesity and the associated health risks, several countries are considering banning fast-food advertising targeting children. In this article, the authors study the effect of such a ban in the Canadian province of Quebec. Using household expenditure survey data from 1984 to 1992, authors examine whether expenditure on fast food is lower in those groups affected by the ban than in those that are not. The authors use a triple difference-indifference methodology by appropriately defining treatment and control groups and find that the ban's effectiveness is not a result of the decrease in fast food expenditures per week but rather of the decrease in purchase propensity by 13% per week. Overall, the authors estimate that the ban reduced fast-food consumption by Us$88 million per year. The study suggests that advertising bans can be effective provided media markets do not overlap.

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)0.003
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 score0.047
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.063
GPT teacher head0.303
Teacher spread0.240 · 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

Citations219
Published2011
Admission routes2
Has abstractyes

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