Truth, Lies, and Videotape: An investigation of the ability of federal parole officers to detect deception.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it