The detection of deception during trials: Ignoring the nonverbal communication of witnesses is not the solution—A response to Vrij and Turgeon (2018)
Bibliographic record
Abstract
In their paper ‘Evaluating credibility of witnesses—Are we instructing jurors on invalid factors?’, Vrij and Turgeon (2018) argue that jurors should be advised not to consider demeanour when trying to evaluate if witnesses are honest or dishonest because of ‘overwhelming scientific evidence’. However, in this response, we contend that substantial empirical scientific studies on nonverbal communication alongside the limitations of deception detection research, as cited by Vrij and Turgeon (2018), undermine their overall argument. While jurors should be warned about erroneous beliefs and dubious concepts on human communication, jurors should also be advised to consider demeanour as a way of enriching their overall understanding of witnesses and their verbal testimony.
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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 | 0.112 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.034 | 0.057 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".