The sociodemographic correlates of conspiracism
Bibliographic record
Abstract
Despite hundreds of studies examining belief in conspiracy theories, it is still unclear who-demographically-is most likely to believe such theories. To remedy this knowledge gap, we examine survey data containing various operationalizations of conspiracism across diverse sociopolitical contexts. Study 1 employs a 2021 U.S. survey (n = 2021) to examine associations between sociodemographic characteristics and beliefs in 39 conspiracy theories. Study 2 similarly employs a survey of 20 countries (n = 26,416) and 11 conspiracy theory beliefs. Study 3 reports results from a 2020 U.S. survey (n = 2015) measuring perceptions about which groups are engaging in conspiracies. Study 4 interrogates data from nine U.S. surveys (2012-2022; n = 14,334) to examine the relationships between sociodemographic characteristics and generalized conspiracy thinking. Study 5 synchronizes studies 1-4 to provide an intersectional analysis of conspiracy theory belief. Across studies, we observe remarkably consistent patterns: education, income, age (older), and White identification are negatively related to conspiracism, while Black identification is positively related. We conclude by discussing why conspiracy theories may appeal most to historically marginalized groups and how our findings can inform efforts to mitigate the negative effects of conspiracy theories.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".