‘Look This Way’: Using Gaze Maintenance to Facilitate the Detection of Children's False Reports
Bibliographic record
Abstract
Summary In two experiments, we investigated whether imposing a secondary task is an effective technique for detecting child deceit. First, 85 children aged 8 to 11 years old provided either a true or false report of a recent school event. At interview, some children were asked to gaze towards either the interviewer's face (IF) or a teddy bear's face (TF), whereas some children were given no gaze instruction. In both the IF and TF conditions, lie‐tellers provided significantly fewer details than truth‐tellers. A total of 192 adult evaluators then judged the credibility of 10 children's reports from one of the three ‘gaze’ conditions with and without guidance on level of detail. Evaluators discriminated truths from lies successfully when judging children instructed to look at IF, but not when children were asked to gaze towards TF. Evaluators who received guidance demonstrated better discrimination between true and false reports than evaluators who received no such information. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".