Communicating scientific uncertainty in a rapidly evolving situation: a framing analysis of Canadian coverage in early days of COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic brought the production of scientific knowledge onto the public agenda in real-time. News media and commentators analysed the successes and failures of the pandemic response in real-time, bringing the process of scientific inquiry, which is also fraught with uncertainty, onto the public agenda. We examine how Canadian newspapers framed scientific uncertainty in their initial coverage of the COVID-19 pandemic and how journalists made sense of the scientific process. METHODS: We conducted a framing analysis of 1143 news stories and opinion during the first two waves of the COVID-19 pandemic. Using a qualitative analysis software, our analysis focused, first, on how scientific uncertainty was framed in hard news and opinion discourse (editorial, op-ed). Second, we compared how specialist health and science reporters discussed scientific evidence versus non-specialist reporters in hard news and columns. RESULTS: Uncertainty emerged as a "master frame" across the sample, and four additional framing strategies were used by reporters and commentators when covering the pandemic: (1), evidence -focusing on presence or absence of it-; (2) transparency and leadership -focusing on the pandemic response-; (3) duelling experts - highlighting disagreement among experts or criticizing public health decisions for not adhering to expert recommendations-; and (4) mixed messaging -criticizing public health communication efforts. While specialist journalists understood that scientific knowledge evolves and the process is fraught with uncertainty, non-specialist reporters and commentators expressed frustration over changing public health guidelines, leading to the politicization of the pandemic response and condemnation of elected officials' decisions. CONCLUSIONS: Managing scientific uncertainty in evolving science-policy situations requires timely and clear communication. Public health officials and political leaders need to provide clear and consistent messages and access to data regarding infection prevention guidelines. Public health officials should quickly engage in communication course corrections if original messages are missing the intended mark, and clearly explain the shift. Finally, public health communicators should be aware of and more responsive to a variety of media reporters, who will bring different interpretative frames to their reporting. More care and effort are needed in these communication engagements to minimize inconsistencies, uncertainty, and politicization.
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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 | 0.028 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".