Industry data on alcohol sales in South Africa between 1995 and 2022 and its value in detecting the impact of policy interventions related to packaging and Covid-19 alcohol availability
Bibliographic record
Abstract
Aims: To assess changes in total alcohol consumed over time, changes in consumption of different alcoholic beverages and the utility of industry sales data to evaluate the impact of policy changes related to a packaging ban on wine products in September 2007 and bans on alcohol sales during the Covid-19 pandemic in 2020 and 2021 in South Africa. Design. Alcohol industry sales data as a proxy for consumption was assessed using statistics presented in South African Wine Industry Information & Systems (SAWIS) booklets released annually between 1995 and 2022 and used to describe changes over time in consumption overall, and by product, and the impact on consumption from changes in policy. Results. Per capita consumption of alcoholic beverages overall has held steady or declined over time, but declines were noted in the market share of wine and beer (especially) and a massive increase for RTDs. The consumption data also indicated short term effects of Covid-19 interventions (especially in 2020) in terms of reducing overall alcohol consumption, with a return to prior levels in 2022. Industry data on packaging for wine was able to show the immediate and longer-term impact of the 2007 ban on wine sold in foil-bags. Conclusion. Industry data are a useful adjunct to consumer measures of alcohol use, as well as in detecting the impact of policy changes related to availability and packaging, notwithstanding gaps in information on illicit/unrecorded sales and other limitations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".