MétaCan
Menu
Back to cohort
Record W2994765595 · doi:10.3390/ijerph17010012

Targeting Children and Their Mothers, Building Allies and Marginalising Opposition: An Analysis of Two Coca-Cola Public Relations Requests for Proposals

2019· article· en· W2994765595 on OpenAlexfundno aff
Benjamin Wood, Gary Ruskin, Gary Sacks

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsOpposition (politics)ScrutinyCoca colaShamePoliticsPublic relationsPublic healthAdvertisingPolitical scienceSociologyBusinessLawMedicine

Abstract

fetched live from OpenAlex

The study provides direct evidence of the goals of food-industry-driven public relations (PR) campaigns. Two PR requests for proposals created for The Coca-Cola Company (Coke) were analysed. One campaign related to the 2016 Rio Olympic Games, the other related to the 2013-2014 Movement is Happiness campaign. Supplementary data were obtained from a search of business literature. The study found that Coke specifically targeted teenagers and their mothers as part of the two PR campaigns. Furthermore, Coke was explicit in its intentions to build allies, particularly with key media organisations, and to marginalise opposition. This study highlights how PR campaigns by large food companies can be used as vehicles for marketing to children, and for corporate political activity. Given the potential threats posed to populations' health, the use of PR agencies by food companies warrants heightened scrutiny from the public-health community, and governments should explore policy action in this area.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.383
Teacher spread0.328 · 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 designQualitative
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

Citations23
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicGlobal Public Health Policies and EpidemiologyFrench-language works237,207