Analyzing natural herd immunity media discourse in the United Kingdom and the United States
Bibliographic record
Abstract
Natural herd immunity, where community-acquired infections in low-risk populations are used to protect high risk populations from infection-has seen high profile support in some quarters, including through the Great Barrington Declaration. However, this approach has been widely criticized as ineffective and misinformed. In this study, we examine media discourse around natural herd immunity in the United States (US) and United Kingdom (UK) to better understand how this approach was promoted. Country-specific news media publications between March 11, 2020 and January 31, 2021 were searched for references to herd immunity. News articles focused on herd immunity and including a stakeholder quote about herd immunity were collected, resulting in 400 UK and 144 US articles. Stakeholder comments were then coded by name, organization, organization type, and concept agreement or disagreement. Government figures and a small but vocal coalition of academics played a central role in promoting natural herd immunity in the news media whereas critics were largely drawn from academia and public health. These groups clashed on whether: natural herd immunity is an appropriate and effective pandemic response; the consequences of a lockdown are worse than those of promoting herd immunity; high-risk populations could be adequately protected; and if healthcare resources would be adequate under a herd immunity strategy. False balance in news media coverage of natural herd immunity as a pandemic response legitimized this approach and potentially undermined more widely accepted mitigation approaches. The ability to protect high risk populations while building herd immunity was a central but poorly supported pillar of this approach. The presentation of herd immunity in news media underscores the need for greater appreciation of potential harm of media representations that contain false balance.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 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".