Not all bullshit pondered is tossed: Reflection decreases receptivity to some types of misleading information but not others
Bibliographic record
Abstract
Abstract Across three studies (N = 659), we present evidence that engaging in explanatory reflection reduces receptivity to pseudo‐profound bullshit but not scientific bullshit or fake news. Additionally, ratings for pseudo‐profound and scientific bullshit attributed to authoritative sources were significantly inflated compared to bullshit from anonymous sources. These findings provide initial evidence that asking people to reflect on why they find certain statements meaningful (or not) helps reduce receptivity to some types of misinformation but not others. Moreover, the appeal of misleading claims spread by perceived experts may be largely immune to the putative benefits of interventions that rely solely on reflective thinking. Taken together, our results suggest that while encouraging the public to be more reflective can certainly be helpful as a general rule, the effectiveness of this strategy in reducing the persuasiveness of misleading or otherwise epistemically‐suspect claims is limited by the type of claims being evaluated.
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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 | 0.009 | 0.090 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".