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Record W4401497641 · doi:10.7895/ijadr.439

Crowdsourcing alcohol billboards in Kampala, Uganda: Examining alcohol advertisement violations

2024· article· en· W4401497641 on OpenAlexvenueno aff
Monica H. Swahn, Alaina Whitton, Rogers Kasirye, Katherine Robaina

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2024
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCrowdsourcingAlcoholAdvertisingAlcohol advertisingPsychologyEnvironmental healthComputer scienceBusinessAlcohol consumptionMedicineBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

Despite high levels of alcohol use in Uganda, there is a scarcity of research on alcohol marketing, its placement and content. In this field study we evaluated the content of alcohol billboards across Kampala, Uganda using the Alcohol Marketing Assessment Rating Tool (AMART). Of the 27 unique alcohol advertisements evaluated, the nine-member review panel found that 23 contained at least one violation yielding a violation rate of 85%. Given the high number of violations, our recommendation is that future alcohol billboard advertisements within Kampala be reviewed and approved by a governing body for compliance with alcohol advertisement standards.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.106
GPT teacher head0.395
Teacher spread0.289 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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