It's all about time: the influence of behaviour and timelines on suspect disclosure during investigative interviews
Bibliographic record
Abstract
Research on confession has usually focused on showing that it is significantly associated with individual, crime-related, and situational/contextual variables and is both a static event and a dichotomous indicator of interview success. Recent work, however, suggests that investigative interviews are a dynamic process in which interrogation strategies change over time. Using a Game Theory perspective, this study looks at the impact of behaviours of both players (interviewer and suspect) on the production of investigation-relevant information (IRI). The sub-objective is to demonstrate the usefulness of applying Game Theory to the study of investigative interviews by considering time and interaction between players as an integrative part of the analysis. Videotaped interviews related to online child sexual exploitation (n = 130) were analysed and the different behaviours of suspects and interviewers were analysed to determine if they involved (1) rapport building/active denial, (2) collaboration, (3) confrontation, (4) emotion/response, and (5) elicitation of information related to the case. Results showed that information relevant to the investigation is often provided shortly after a suspect has offered additional information or given responses that meet emotional needs (e.g. justifications). The interviewer's use of available evidence increases the likelihood that additional information will be provided, while the ability to build a rapport with the suspect is effective in the longer term, even if a positive effect is not immediately observed. Using a dynamic process approach in analysing investigative interviews provides a starting point for the creation of practical guidelines to help practitioners increase suspect collaboration during investigative interviews.
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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 | 0.023 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".