Analysis of crisis communication by the Prime Minister of Australia during the COVID-19 pandemic
Bibliographic record
Abstract
Leadership and communication capabilities of federal leaders during crises are imperative to support and guide citizens' behaviors and emotions. The following content analysis examines crisis communication delivered by the Australian Prime Minister (PM), Scott Morrison during the COVID-19 pandemic. Communication delivered over seven months starting from the first reported case of COVID-19 in Australia, was analyzed through a process of coding to identify central organizing crisis communication frames and themes and measured against eleven main themes based on principles of Crisis and Emergency Risk Communication (CERC) recommended by the WHO and US Centers for Disease Control and Prevention. Transcripts were sourced from the PM's official website and 91 communiques were analyzed. Key epidemiological indicators and public health measures were reviewed over timeframe to examine changes in communication over the pandemic. Findings indicated that PM Morrison included many features of CERC within his official messaging. Our analysis revealed that the original framework was limited in its scope to encompass certain messages and thus the allocation of new frames,'public health and medical advice' and 'assuring and commending the public and institutions', allowed for a more thorough analysis of communication during a novel global health pandemic. The temporal analysis demonstrated that the government's policy and communication temporally followed case numbers and relative threat of the virus. This study has provided an in-depth review of CERC during the first phase of the COVID-19 pandemic. New frames and themes for the current CERC framework are suggested which can be transferable to other crises in Australia and other 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".