Who Achieved Covid-19 Success? A Comparative Analysis Of The Communication Styles, Rhetoric And Crisis Response Of Three Prime Ministers And What we Can Learn From The Leadership Of Australia, Canada And New Zealand
Bibliographic record
Abstract
The coronavirus crisis of 2020/21 has been the largest global crisis in living memory. By summer 2021, nearly 200,000,000 cases of COVID-19 had been reported worldwide and more than 4,000,000 people died (John Hopkins Coronavirus Resource Centre, 2021). The governments of Australia and New Zealand, led by Prime Ministers Scott Morrison and Jacinda Ardern, implemented an early pandemic response that clearly communicated what the situation was and why it was crucial to act immediately, leading to a significantly reduced number of deaths and cases. In contrast, Canadian Prime Minister Justin Trudeau was delayed in his initial response. His messaging was largely focused on offering financial support and incentives as opposed to adding context to the situation. This study aims to offer a preliminary understanding of the communication strategies and tactics that were used by the Australian and New Zealand government leaders to drastically reduce the number of COVID19 case counts and deaths in their countries.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".