Halfe the world knowes not how the other halfe lies: Investigation of verbal and non‐verbal signs of deception exhibited by criminal offenders and non‐offenders
Bibliographic record
Abstract
Purpose. This study examined the verbal and non‐verbal behaviours exhibited by criminal offender and non‐offender participants while they related planned truthful and deceptive accounts about emotional autobiographical events. Methods. In a 2 × 2 (participant group × veracity) quasi‐experimental design, offenders ( N = 27) and university students ( N = 38) provided videotaped accounts of four autobiographical emotional events: two honest and two fabricated (counterbalanced). Patterns of behaviour exhibited during the truthful and the deceptive accounts were then compared. Results. In general, offenders and non‐offenders showed similar patterns of deceptive behaviour. Deceptive accounts by both groups contained fewer details than honest accounts. Deception was associated with an increase in illustrator usage and self‐manipulations; however, univariate analyses indicated only that offenders exhibited significantly more self‐manipulations when lying. A significant interaction emerged in which offenders showed a reduction in smiles when lying about the emotional events, while students showed no difference. Conclusions. Offenders and students showed similar patterns of lying on most cues. However, unlike non‐offenders, offenders smiled less and showed an increase in self‐manipulations when lying. We theorize that offenders may have been aware that smiling and laughing are negatively related to perceived credibility in the speaker and used self‐manipulations to distract listeners from the content of their lies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".