Spikey blobs with evil grins: understanding portrayals of the coronavirus in South African newspaper cartoons in relation to the public communication of science
Bibliographic record
Abstract
This study explores how South African newspaper cartoonists portrayed the novel coronavirus during the initial months of the COVID-19 pandemic. We show how these cartoons respond to the socio-economic and cultural contexts in the country. Our analysis of how cartoonists represent the novel coronavirus explain how they create meaning (and may influence public sentiments) using colour, morphological characteristics and anthropomorphism as visual rhetorical tools. From a total population of 497 COVID-19-related cartoons published in 15 print and online newspapers from 1 January to 31 May 2020, almost a quarter (24%; n=120) included an illustration of the coronavirus. Viruses were typically coloured green or red and attributed with human characteristics (most often evil-looking facial expressions) and with exaggerated, spikey stalks surrounding the virus body. Anthropomorphism was present in more than half of the 120 cartoons where the virus was illustrated (58%; n=70), while fear was the dominant emotional tone of the cartoons. Based on our analysis, we argue that editorial cartoons provide a useful source to help us understand the broader discursive context within which public communication of science operates during a pandemic.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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".