Beliefs about deception in Australia
Bibliographic record
Abstract
Research addressing beliefs about deception has been mostly conducted with North American and European participants. However, deception belief consequences are not continent-bound. In legal proceedings, when jurors are responsible for assessing witness credibility, beliefs about deception can distort the outcome of jury trials, which are integral to Australia's criminal justice system. Research on beliefs about deception in Australia are scarce. Therefore, this article aims to address this gap by replicating the second study of The Global Deception Research Team with Australian participants. Five hundred and twenty-eight Australian participants responded to the 10-question questionnaire from the second study, with 84.7% stating they could usually tell when someone is lying to them. However, 75.6% also acknowledged that it is more difficult to tell when someone from a different cultural background is lying. Most Australian participants relied on three nonverbal cues to detect lying: increased posture shifting, increased self-touching and scratching and decreased eye contact. We discuss the implications of the results for research on lie detection in Australia and for research on deception outside North America and Europe.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".