Honest People Tend to Use Less—Not More—Profanity
Bibliographic record
Abstract
This article shows that the conclusion of Feldman et al.'s (2017) Study 1 that profane individuals tend to be honest is most likely incorrect. We argue that Feldman et al.'s conclusion is based on a commonly held but erroneous assumption that higher scores on Impression Management Scales, such as the Lie Scale, are associated with trait dishonesty. Based on evidence from studies that have investigated (1) self-other agreement on Impression Management Scales, (2) the relation of Impression Management Scales with personality variables, and (3) the relation of Impression Management Scales with objective measures of cheating, we show that high scores on Impression Management Scales are associated with high-instead of low-trait honesty when measured in low-stakes conditions. Furthermore, using two data sets that included an "I never swear" item, we show that profanity use is negatively related to other reports of HEXACO honesty-humility and positively related to actual cheating.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".