‘You can’t bullshit a bullshitter’ (or can you?): Bullshitting frequency predicts receptivity to various types of misleading information
Bibliographic record
Abstract
Research into both receptivity to falling for bullshit and the propensity to produce it have recently emerged as active, independent areas of inquiry into the spread of misleading information. However, it remains unclear whether those who frequently produce bullshit are inoculated from its influence. For example, both bullshit receptivity and bullshitting frequency are negatively related to cognitive ability and aspects of analytic thinking style, suggesting that those who frequently engage in bullshitting may be more likely to fall for bullshit. However, separate research suggests that individuals who frequently engage in deception are better at detecting it, thus leading to the possibility that frequent bullshitters may be less likely to fall for bullshit. Here, we present three studies (N = 826) attempting to distinguish between these competing hypotheses, finding that frequency of persuasive bullshitting (i.e., bullshitting intended to impress or persuade others) positively predicts susceptibility to various types of misleading information and that this association is robust to individual differences in cognitive ability and analytic cognitive style.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".