Alcohol Sports sponsorship in Uganda: A case study analysis
Bibliographic record
Abstract
Aims: This study aimed to explore the nature and extent of alcohol sports sponsorship in Uganda and the activities involved. Methods: Utilising an exploratory case study methodology to document examples of alcohol industry activity related to sports sponsorship in Uganda. This study employed desk-based reviews of publicly available examples of sponsorship activities from websites and social media sites as well as visits to sporting venues to observe alcohol sponsorship practices. Results: Sports sponsorship by alcohol companies was found to be a common practice in Uganda. Across the sample of data included in this study, we observed multiple sponsorship deals within a range of professional sports, including football, basketball and golf. Across these sponsorship arrangements, several activities explicitly promoted products or subtly blended them amongst other stimulating content. This included: a presence on social media channels; limited edition products; alcohol brand logos on match strips; advertising and promotion at various locations inside and outside the sporting venues; and alcohol industry representatives featuring in news reports. Conclusion: Alcohol sports sponsorship in Uganda is widespread and multi-faceted. The types of sponsorship activities observed in this study mirror those used around the world. The transnational companies involved use sports sponsorship to position themselves as central to Ugandan economy, culture, heritage and the sustainability of sports across the country. Understanding how sports sponsorship is used to promote alcohol brands across Uganda is important to inform future policy decisions regarding alcohol marketing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".