Truth, Lies, and Videotape: An investigation of the ability of federal parole officers to detect deception.
Bibliographic record
Abstract
The ability of a group of Canadian federal parole officers to detect deception was investigated over the course of 2 days of lie detection training. On the first day of training, 32 officers judged the honesty of 12 (6 true, 6 fabricated) videotaped speakers describing personal experiences, half of which were judged before and half judged after training. On the second day, 5 weeks later, 20 of the original participants judged the honesty of another 12 videotapes (again, 6 pre- and 6 posttraining). To isolate factors relating to detection accuracy, three groups of undergraduate participants made judgments on the same 24 videotapes: (1) a feedback group, which received feedback on accuracy following each judgment, (2) a feedback + cue information group, which was given feedback and information on empirically based cues to deception, and (3) a control group, which did not receive feedback or cue information. Results indicated that at baseline all groups performed at or below chance levels. However, overall, all experimental groups (including the parole officers) became significantly better at detecting deception than the control group. By the final set of judgments, the parole officers were significantly more accurate (M = 76.7%) than their baseline performance (M = 40.4%) as well as significantly more accurate than the control group (M = 62.5%). The results indicate that detecting deceit is difficult, but training and feedback can enhance detection skills.
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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 | 0.017 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".