The Bullshitting Frequency Scale: Development and psychometric properties
Bibliographic record
Abstract
Recent psychological research has identified important individual differences associated with receptivity to bullshit, which has greatly enhanced our understanding of the processes behind susceptibility to pseudo-profound or otherwise misleading information. However, the bulk of this research attention has focused on cognitive and dispositional factors related to bullshit (the product), while largely overlooking the influences behind bullshitting (the act). Here, we present results from four studies focusing on the construction and validation of a new, reliable scale measuring the frequency with which individuals engage in two types of bullshitting (persuasive and evasive) in everyday situations. Overall, bullshitting frequency was negatively associated with sincerity, honesty, cognitive ability, open-minded cognition, and self-regard. Additionally, the Bullshitting Frequency Scale was found to reliably measure constructs that are (1) distinct from lying and (2) significantly related to performance on overclaiming and social decision tasks. These results represent an important step forward by demonstrating the utility of the Bullshitting Frequency Scale as well as highlighting certain individual differences that may play important roles in the extent to which individuals engage in everyday bullshitting.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".