Government messaging about COVID-19 vaccination in Canada and Australia: a Narrative Policy Framework study
Bibliographic record
Abstract
Abstract Background Storytelling and narratives are critical components to public policy and have been central to public policy communicators throughout the COVID-19 pandemic. Aim This study applied the Narrative Policy Framework to compare and contrast the policy narratives of the Canadian and Australian Prime Ministers regarding COVID-19 vaccination. Methods Official media releases, transcripts and speeches published on the websites of Prime Minister Morrison and Prime Minister Trudeau between 31 August 2020 and 10 September 2021 relating to COVID-19 vaccines were thematically analysed according to the Narrative Policy Framework. Results The policy narratives of Scott Morrison and Justin Trudeau tended towards describing both governments as heroes for securing and rolling out vaccines. Trudeau tended to focus on the villain of COVID-19 while Morrison regularly described other countries as victims of COVID-19 to position Australia as superior in its decision-making. These findings also demonstrate how narratives shifted over time due to changing COVID-19 case numbers, emergence of rare complications associated with the AstraZeneca vaccine and as new information arose. Conclusion These findings offer lessons for COVID-19 times as well as future pandemics and disease outbreaks by providing insight into how policy narratives influenced policy processes in both Australia and Canada.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".