Engaging in a newspaper cartoon thematic analysis to understand the socioeconomic, health, political, and environmental impact of COVID-19 in Sri Lanka
Bibliographic record
Abstract
Introduction: Newspaper cartoons are viewed as an effective means of communicating important social concerns. Method:An exploratory content analysis of newspaper cartoons was done during the COVID-19 outbreak in Sri Lanka . Cartoons from eight daily online newspapers published from 15th of March to 15th of April 2020 were included. “corona”, “COVID-19”, or “Sri Lanka” were used as keywords. Thematic analysis was used to identify the different perspectives of cartoons. Results: Background of COVID-19 pandemic, health, social, economic, political, and environmental aspects emerged within a sample of 87 cartoons. Thirteen common themes were identified as COVID-19 as a pandemic, COVID-19 has become a crisis for Sri Lanka, prevention is the priority, knowledge deficit leads to breaking health advice, efforts to control virus outbreak, shifting socio-cultural practices, effects of lockdown, rich man’s virus – poor suffer the most bringing the global economy to its knee, coronavirus is driving the black market for essential stuff, seeking strategies to hold the election, applying health precautions in election activities and other political involvements. Conclusion: Cartoons were effectively used to give broad health and other messages to the public during the COVID-19 outbreak in Sri Lanka. Newspaper cartoons can be used as an effective mode of disseminating information during a crisis.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".