Reorienting the Study of Conspiratorial Thinking in Psychology: From Contaminated Mindware to Belief in Hidden Causal Forces
Bibliographic record
Abstract
ABSTRACT In this study, we combined the perspectives of psychology and political science to study partisan conspiracy beliefs and to examine the predictors of belief in both true and false nonpartisan conspiracies. From political science, we explored the recently investigated variable of antiestablishment attitudes as well as two political attitudes unexplored in research on conspiratorial thinking: utopianism and government credulity. From psychology, we examined variables that have been consistent predictors in previous research on conspiracy belief: actively open‐minded thinking, paranormal beliefs, and the Dark Triad. Actively open‐minded thinking was a potent predictor of adaptive epistemic outcomes. We also included a scale derived and adapted from previous work on conspiratorial mentality that was designed to measure the broad‐based conspiratorial thinking trait that we posit underlies most specific conspiracy beliefs: the Hidden Causal Forces scale. We found that the path model that best explained the observed correlations depends strongly on whether the conspiracy is partisan or nonpartisan and, in the case of nonpartisan conspiracies, whether the model seeks to explain implausible false conspiracy beliefs, true conspiracy beliefs, or the ability to discriminate between true and false conspiracies.
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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 | 0.003 | 0.012 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".