From Starmer's scuffle to the Siege of Ottawa: the resistible rise of the anti-vaxxers
Bibliographic record
Abstract
This article discusses the energy and aggressiveness of the campaigns against mitigating transmission of Covid-19 during the pandemic, and especially against vaccination. These campaigns have brought anti-vaxx sentiments into the limelight with a vengeance. The aim of the article is to seek a better understanding of the forms that the contemporary anti-vaxx movements can take and what is fuelling them. It has three parts. The first describes recent anti-vaxxer actions in the UK, France, Austria and Canada, highlighting the siege of Ottawa. The second part draws on the history of anti-vaxx movements in the UK and the USA for insights into their militancy; and in the third part there is an exploration of ideas about what makes anti-vaxx platforms so attractive and who is drawn towards them. Countering anti-vaxxer propaganda requires an understanding of the pathways by which the information spreads. False narratives will move from anti-vaccine echo chambers to mass audiences via social-media sharing, as well as coverage in local and mainstream media. Countering this misinformation all along the pathway requires a whole-of–society effort that is multifaceted, tolerant of short-term setbacks, and persistent even in challenging conditions. The NHS faces the whole of society. Could it, in its present state, be the vehicle for engaging the anti-vaxxer movements?
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".