Do People Answer Honestly When Asked About a No Honest Behavior?
Bibliographic record
Abstract
Abstract: Recently assessment of bullshitting has gained considerable interest. However, self-report measures of bullshitting are at risk of socially desirability (SD) responding because people are questioned about a sensitive and personal topic (i.e., their tendency to embellish or modify the truth to portray themselves positively). This study aims to identify the impact of SD on the Bullshitting Frequency Scale (BSF) to ensure that the scale can provide sound measures. Italian ( N = 298, 69.80% female) and English participants ( N = 301, 72.76% female) were recruited. To overcome some limitations of the classical procedures used to deal with SD, factor analysis was employed to identify and control this response bias. At the same time, the validation of the Italian version of the BSF (BSF-I) was conducted to provide evidence that the scale maintains its psychometric properties once translated into Italian. Factor analysis confirmed the two-factor structure of the scale and factor loadings indicated a minimal impact of SD. Multidimensional IRT showed moderate to excellent MDISC values and strong conditional reliability. Multigroup CFA indicated strict invariance across genders and metric invariance across Italian and English versions. The BSF-I showed sound psychometric properties and these findings suggest that people answer honestly when asked about 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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".