Memory Instructions, Vocalization, Mock Crimes, and Concealed Information Tests with a Polygraph
Bibliographic record
Abstract
Accuracy rates with polygraphs using concealed information tests (CITs) depend on memory for crime details. Some participants read instructions on murdering a dummy victim that specified exact crime details asked on the subsequent CIT. Others read instructions not stating details, but still requiring interaction with the exact same details for the crime. For example, the murder weapon was under four heavy boxes. Instructions stated either "... remove the 4 boxes ..." or "... remove the boxes ..." Thus, each group removed four boxes, but only one group was primed with the number "4" beforehand. In addition, the victim unexpectedly shouted at some participants during the crime. An innocent group was not exposed to either manipulation. Memory, detection scores, and detection rates were lower for guilty participants not primed with details. Sound affected detection scores but not memory, and there was no interaction between the two factors. Information tests are limited by how crime information is received.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".