Analysing the ‘follow the science’ rhetoric of government responses to COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At the beginning of the COVID-19 pandemic, many leaders claimed that their public health policy decisions were ‘following the science’; however, the literature on evidence-based policy problematises the idea that this is a realistic or desirable form of governance. This article examines why leaders make such claims using Christopher Hood’s (2011) blame avoidance theory. Based on a qualitative content analysis of two national newspapers in each of Australia, Canada and the UK, we gathered and focused on unique moments when leaders claimed to ‘follow the science’ in the first six months of the pandemic. We applied Hood’s theory to identify the types of blame avoidance strategies used for issues such as mass event cancellation, border closures, face masks, and in-person learning. Politicians most commonly used ‘follow the science’ to deflect blame onto processes and people. When leaders’ claims to ‘follow the science’ confuse the public as to who chooses and who should be held accountable for those decisions, this slogan risks undermining trust in science, scientific advisors, and, at its most extreme, representative government. This article addresses a gap in the literature on blame avoidance and the relationship between scientific evidence and public policy by demonstrating how governments’ claims to ‘follow the science’ mitigated blame by abdicating responsibility, thus risking undermining the use of scientific advice in policymaking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it