Newsprint representation of the alcohol sales bans during the COVID-19 pandemic in South Africa: A mixed methods analysis
Bibliographic record
Abstract
Aims: Content analysis of newspapers covering the alcohol sales bans during Covid-19 in South Africa.
 Methods: Mixed method content analysis of the highest circulated, paid for, English language newspapers published by four newspaper outlets in South Africa, between 26 February to 26 September 2020 (seven months).
 Setting: South African Bibliographic Information Network (Sabinet) and Arena Holdings Media databases were used.
 Participants: 317 newspaper articles were identified for analysis from four newspapers: Sunday Times (Arena Holdings), Daily Sun (Naspers), The Star (Independent Media) and the Mail and Guardian (Media Development Investment Fund).
 Measures: Qualitative data: a structured coding frame was used to identify themes. Quantitative data: date, agency, placement/page number in the newspaper, number of graphics, words in heading and in article and whether the article was based on opinion or fact. The media vectors: framing, responsibility and newspaper media exposure were calculated.
 Findings: Articles were predominantly unfavourable toward the alcohol sales bans, indicated that government should take responsibility for prevention of harmful alcohol use, and focused mainly on the negative economic impact of the alcohol sales bans.
 Conclusions: News agencies should make a concerted effort to ensure balanced reporting on matters of health and put measures in place to prevent undue influence on journalists by large corporations, such as the alcohol industry.
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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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".