Sketching routes to elicit information and cues to deceit
Bibliographic record
Abstract
Abstract Sketching while narrating involves describing an event while sketching on a blank paper (self‐generated sketch) or on a printed map. We compared the effects of self‐generated sketches and printed maps on information elicitation and lie detection. Participants ( N = 211) carried out a mock mission and were instructed to tell the truth or to lie about it in an online interview. In the first phase of the interview, all participants provided a free recall. In the second phase, participants provided another free recall or verbally described the mission while sketching on a blank paper or on a printed map. Truth tellers provided richer accounts than lie tellers. Larger effect sizes emerged for the self‐generated sketch condition than for the printed map and free recall conditions. This suggests that self‐generated sketches are more effective lie detection tools when information on routes and locations is sought.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; both teacher heads agree on what is shown here.
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".