Does deliberation decrease belief in conspiracies?
Bibliographic record
Abstract
What are the underlying cognitive mechanisms that support belief in conspiracies? Common dual-process perspectives suggest that deliberation helps people make more accurate decisions and decreases belief in conspiracy theories that have been proven wrong (therefore, bringing people closer to objective accuracy). However, evidence for this stance is i) mostly correlational and ii) existing causal evidence might be influenced by experimental demand effects and/or a lack of suitable control conditions. Furthermore, recent work has found that analytic thinking tends to increase the coherence between prior beliefs and new information, which may not always lead to accurate conclusions. In two studies (Study 1: N = 1028; Study 2: N = 1000), participants were asked to evaluate the strength of conspiracist (or non-conspiracist) explanations of events. In the first study, which used well-known conspiracy theories, deliberation had no effect. In the second study, which used relatively unknown conspiracy theories, we found that experimentally manipulating deliberation did increase belief accuracy - but only among people with a strong ‘anti-conspiracy’ or strong ‘pro-conspiracy’ mindset from the outset, and not among those with an intermediate conspiracist mindset. Although these results generally support the idea that encouraging people to deliberate can help to counter the growth of novel conspiracy theories, they also indicate that the effect of deliberation on conspiracist beliefs is more complicated than previously thought.
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".