Examining the effects of evidence disclosure timing and strength on information inconsistencies and provision within investigative interviews
Bibliographic record
Abstract
Late disclosure of evidence within investigative interviews with guilty suspects has been shown to increase statement-evidence and within-statement inconsistencies, which are indicators of deception. We experimentally tested whether such inconsistencies were influenced by the timing of evidence disclosure and strength of the evidence. We also tested whether evidence disclosure timing or strength had any effect on the provision of novel investigative information, or the rapport and trust between interviewer and interviewee. We employed a 2(Evidence disclosure timing: Early vs Late) x 2(Evidence strength: Weak vs Strong) between-participants design. Participants (N = 101) role-played a suspect guilty of theft and were interviewed via videoconference. Participants were instructed to convince the interviewer that they were innocent. Late disclosure of evidence led to more statement-evidence inconsistencies and within-statement inconsistencies than early evidence disclosure. Evidence disclosure timing did not affect rapport or the provision of novel investigative information. There were no clear indications of the impact of evidence strength, however, we observed that the manipulations of evidence proximity and reliability did not consistently impact perceptions of the evidence’s strength.
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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 | 0.035 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".