A Content Analysis and Population Exposure Estimate Of Guinness Branded Alcohol Marketing During the 2019 Guinness Six Nations
Bibliographic record
Abstract
AIMS: To quantify Guinness-related branding in the 2019 Guinness Six Nations Championship. METHODS: Content analysis of Guinness-related branding ('Guinness' and the alibi brand 'Greatness') was shown during active play throughout all 15 games of the 2019 Guinness Six Nations Championship. The duration of each appearance was timed to the nearest second to provide information on the amount of time that Guinness-related branding was shown on screen. Census data and viewing figures were used to estimate gross and per capita alcohol impressions. RESULTS: Our coding identified a total of 3719 appearances of two logos of which 3415 (92%) were for 'Guinness' and 304 (8%) were for 'Greatness'. 'Guinness' imagery was present for 13,640 s (227.3 min or 3.8 h, 16% of total active play time), 'Greatness' was present for 944 s (15.7 min, 1% of total active play time), with a combined total of 14,584 s across all games (243 min or 4.05 h, 17% of active play time). The 15 games delivered an estimated 122.4 billion Guinness-related branded impressions to the UK population, including 758 million to children aged under 16. CONCLUSIONS: Alcohol marketing was highly prevalent during the 2019 Guinness Six Nations Championship and was a significant source of exposure to alcohol marketing and advertising for children, likely influencing youth alcohol experimentation and uptake.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".