News Coverage of Covid-19 and Swine Flu: A Corpus-Assisted Study
Bibliographic record
Abstract
The progression rate of Covid-19 and Swine Flu to the advanced pandemic level intensified the risk and damage to human fatalities. Since their outbreak, media outlets spared no effort in reporting news and updates about the two diseases. This study investigates the representations of Covid-19 and Swine Flu pandemics in The New York Times and The Guardian newspapers by utilizing a corpus linguistic quantitative approach. The most monthly read article was collected over one year since the two diseases were declared pandemics by the World Health Organization. The data were compiled in a corpus and analysed using Wordsmith software based on the concordances and the frequency list of the top 15 frequent words in each pandemic. The frequency lists generated 9 themes including: reporting verbs, titles, places, quantity expressions, people, disease name, prevention and control, impact and modal verbs. The study found that news tended to spread fear among people and highlight the governments' roles during Covid-19 pandemic more than Swine Flu.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".