On the relationship between age and conspiracy beliefs
Bibliographic record
Abstract
Abstract Research on conspiracy theories has long turned a blind eye on the role of age in explaining conspiracy beliefs. Few studies include age and those that do have yet to consider how and why age matters when it comes to the spread of conspiracy theories. In this article, we investigate the relationship between age and conspiracy beliefs with two complementary studies. In Study 1, we conduct a meta‐analysis of a large sample of studies on conspiracy beliefs published between 2014 and 2024 ( k = 191; N = 374,224). The results reveal a small but robust negative association between age and conspiracy endorsement. In Study 2, we use an original multinational survey to investigate three potential mechanisms that may explain the relationship between age and conspiracy beliefs ( N = 6098). We explain youth's higher beliefs in conspiracy theories by their predisposition to unconventional styles of political participation, lower levels of self‐esteem, and general political disaffection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".