Do people sincerely believe conspiracy theories that they endorse?
Bibliographic record
Abstract
Survey data are routinely used to estimate the prevalence of belief in conspiracy theories and to test hypotheses about the correlates, causes, and consequences of these beliefs. However, concerns have been raised about whether people who endorse conspiracy theories sincerely believe them. We examined sincerity in a survey of 1,044 Australians. We found that endorsement of six pre-existing conspiracy theories was widespread, with 33.4% of participants endorsing at least one of them and 5.0% endorsing all six. However, we also found evidence that a sizable proportion of participants who endorsed conspiracy theories might not sincerely believe them. Endorsement of a highly bizarre conspiracy theory about a Canadian raccoon army that we invented ourselves was a strong predictor of 1) endorsing pre-existing conspiracy theories, 2) endorsing conspiracy theories that clearly contradict each (coronavirus is a myth and coronavirus is spread by 5G technology), and 3) self-reported insincere responding. Self-reported insincere responding during the survey was also a strong predictor these outcomes. Overall, our results suggest that endorsement of conspiracy theories in surveys does not imply sincere belief, which has implications for interpreting work in this literature.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".