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Record W2614731011 · doi:10.1016/j.ypmed.2017.05.011

Energy drink consumption and marketing in South Africa

2017· article· en· W2614731011 on OpenAlexfundno aff
Nicholas Stacey, Corné van Walbeek, Mashekwa Maboshe, Aviva Tugendhaft, Karen Hofman

Bibliographic record

VenuePreventive Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsConsumption (sociology)AdvertisingMedicineEnergy (signal processing)Energy consumptionOddsEnvironmental healthMarketingLogistic regressionBusinessEngineering

Abstract

fetched live from OpenAlex

Energy drinks are a fast-growing class of beverage containing high levels of caffeine and sugar. Advertising and marketing have been key to their growth in South Africa. This paper documents trends in energy drink consumption and energy drink advertising, and examines the relationship between exposure to energy drink advertising and consumption. Logistic regressions were estimated of categories of energy drink consumption on individual characteristics, as well as exposure to energy drink advertising. Exposure to advertising is measured by reported viewing of channels high in energy drink advertising. Energy drink consumption in South Africa is higher among younger, wealthier males. Spending on energy drink advertising is mostly focused on television. Targeted channels include youth, sports and general interest channels. Viewers of channels targeted by energy drink advertisers have higher odds of any and moderate levels of energy drinks consumption.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.214
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.062
GPT teacher head0.352
Teacher spread0.290 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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