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Record W2141090995 · doi:10.1111/cjag.12078

An Evaluation of the Effect of Child‐Directed Television Food Advertising Regulation in the United Kingdom

2015· article· en· W2141090995 on OpenAlexvenueaboutno aff
Andrés Silva, Lindsey M. Higgins, Mohamud Hussein

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsHFSSPer capitaAdvertisingTelevision advertisingQuarter (Canadian coin)BusinessEconomicsAgricultural economicsDemographic economicsMedicineEnvironmental healthEngineeringTelecommunicationsGeography

Abstract

fetched live from OpenAlex

Faced with increasing rates of childhood obesity, the U.K. government has recently introduced stricter regulations to reduce children's exposure to advertising of foods high in fat, sugar, and salt (HFSS). The purpose of our paper is to quantify the impact of HFSS regulations on household expenditures in the three sequential “phases” that reflect the evolution of the U.K. regulatory regime: a period of no regulation, a period of voluntary self‐regulation at company level, and the current coregulation implemented by the industry code of practice. Results suggest that coregulation is the only regulatory mechanism that leads to a significant reduction in advertising expenditures. The reduction in total advertising of £11.4 million is composed of an £15.2 million decrease in television (TV) HFSS advertising expenditures. This decline in TV advertising is partially compensated by other media, which translates to a change in household HFSS food and drink expenditures. However, self‐regulation and coregulation lead to reduction on HFSS expenditure. As a result of regulation, households without children decrease HFSS drink expenditures by £5.2 per capita (per quarter), while households with children decrease per capita HFSS food expenditures by £14.9 and HFSS drink expenditures by £5.6 (per quarter).

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 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)0.004
metaresearch head score (Gemma)0.001
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 score0.534
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.043
GPT teacher head0.198
Teacher spread0.155 · 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 teacher head, 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

Citations24
Published2015
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicEconomics of Agriculture and Food MarketsFrench-language works237,207