Allaying the Fears Metaphorically: Representation of Coronavirus Crisis in Saudi English Newspapers
Bibliographic record
Abstract
The present study is an analysis of the representation of the crises brought about by the coronavirus disease (Covid-19) at the socio-economic front in Saudi Arabia and the world over, in two Saudi English dailies, Saudi Gazette and Arab News. The study analyses the use of metaphors in the language employed in reporting the news items or presentation of expert opinions on the virus and the disease, and the deeper significance of the use of such metaphors in writing on the pandemic and its causes. Cognitive Metaphor Theory (CMT) has been employed as framework of analysis and the data have been analysed using Pragglejaz Group’s MIP. Six news and opinionated items (two from Saudi Gazette and four from Arab News) have been analysed. The analysis shows that the crisis writing relies heavily on war metaphors, Sinophobia metaphors, and metaphors allaying fears. The metaphorical language used in the selected English dailies plays a big role in allaying the public fears on the spread of the disease and putting the government programs in the right perspective. The use of metaphorical language to talk about the pandemic and its potent causes has been quite effective in addressing the sensitive issues since human psyche displays deep-set prejudices against certain panic conditions and social formations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".