Spikey blobs with evil grins: understanding portrayals of the coronavirus in South African newspaper cartoons in relation to the public communication of science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it