Conviction without confession: a case example of information seeking dialogue-based interviewing
Bibliographic record
Abstract
Purpose Suspect interviewing in North America has evolved from coercive tactics to guilt-presumptive methods and, more recently, to information seeking dialogue-based (ISDB) approaches such as the PEACE model. Such approaches prioritize open dialogue and comprehensive suspect accounts over confession-driven strategies. These methods have been shown to reduce the risk of false confessions and enhance the quality of investigative information, though they are sometimes criticized for being “too soft” or insufficiently tested in real-world settings. This paper aims to explore the real-world application of an ISDB approach in the high-stakes interview of Adam Strong, who was ultimately convicted of first-degree murder and manslaughter. Design/methodology/approach Using PEACE as a framework, the authors detail how Detective Paul Mitton skillfully used rapport-building, strategic evidence presentation and open dialogue to elicit admissions without coercion or confrontation. Findings Although Strong did not confess to the homicides or discuss how the victims died, the admissions he provided during the 12-h interview were central to the court’s guilty rulings. Research limitations/implications Though a single-case analysis, this paper underscores the necessity for further empirical research on ISDB approaches across diverse real-world scenarios. Practical implications This case highlights how an ISDB approach can generate critical evidence while meeting both investigative and legal standards. The authors believe it underscores that the future of suspect interviewing lies in the continued adoption and refinement of approaches that prioritize rapport-building and open, free-flowing dialogue while incorporating safeguards to ensure the admissibility of the interview. Originality/value This paper presents a unique and practical application of an ISDB approach, contributing valuable insights for practitioners and researchers into advancing ethical and effective suspect interviewing practices.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".