Ability to detect fake news predicts sub-national variation in COVID-19 vaccine uptake across the UK
Bibliographic record
Abstract
Abstract Susceptibility to believing false or misleading information is associated with a range of adverse outcomes. However, it is notoriously difficult to study the link between susceptibility to misinformation and consequential real-world behaviors such as vaccine uptake. In this preregistered study, we devise a large-scale socio-spatial model that combines the rigor of a psychometrically validated test of misinformation susceptibility administered to a nationally representative sample of 16,477 individuals with COVID-19 vaccine uptake data of 129 sub-national regions published by the United Kingdom (UK) government, to show that the general ability to detect misinformation strongly and positively predicts regional vaccine uptake in the UK. We put this practically significant correlational effect size into perspective by noting how psychological interventions that reduce individuals’ misinformation susceptibility could be associated with additional vaccine uptake.
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How this classification was reachedexpand
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".