Who is marketing alcohol in the slums of Kampala? A closer look at marketing types, content and brands
Bibliographic record
Abstract
Objective: To understand alcohol marketing strategies used in an urban slum, photographs of street-level (defined as being seen while walking along the streets) alcohol advertisements were assessed to determine the marketing types, content, and brands in Kampala, Uganda. The content of these photographs was categorized and analyzed to understand the strategies implemented in marketing alcoholic beverages at the street-level in the community. Methods: We collected pilot data in May 2019 of the content and placements of alcohol advertisements in urban slums using smart phones. Three teams of researchers walked a set route of four stretches of 100 meters surrounding the boda boda (motorcycle) taxi stand in the urban retail center in three selected areas in Makindye in Kampala, Uganda. After the data collection, the photographs were reviewed, categorized, and simple, descriptive statistics were computed. Results: Across three locations, 181 photos of alcohol advertisements were taken with 129 of the photos meeting the criterion for analysis. The most common marketing message was focused on the product itself with quality, taste, and national pride being the top three sub-categories. Overall, 80% of the advertisements were posters found outside bars, restaurants, or supermarkets. Of the products advertised, 75% of the products were produced by one of two companies: Diageo or AB InBev. Conclusions: The approach for capturing and coding alcohol marketing in urban slums can be refined and used in future research. Also, the approach can be instrumental for characterizing the alcohol environment at a specific time or for continuous monitoring of marketing to inform and evaluate intervention strategies aimed at reducing alcohol advertisement exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".