Improving the Interviewing of Suspects Using the PEACE Model: A Comprehensive Overview
Bibliographic record
Abstract
In light of psychological research, a growing number of countries/organizations have decided to adopt a model/approach of “investigative interviewing” of suspects that does not rely on coercive or oppressive methods. In 2016, the United Nations’ “Special Rapporteur on torture and other cruel, inhumane or degrading treatments” (law professor Juan Mendez) submitted his report to the United Nations, which stated that “The Special Rapporteur … advocates the development of a universal protocol identifying a set of standards for non-coercive interviewing methods and procedural safeguards that ought, as a matter of law and policy, to be applied at a minimum to all interviews by law enforcement officials, military and intelligence personnel and other bodies with investigative mandates.” When mentioning this “universal protocol” in 2016, the U.N. Special Rapporteur noted that “The essence of an alternative information-gathering model was first captured by the PEACE model of interviewing adopted in 1992 in England and Wales … investigative interviewing can provide positive guidance for the protocol.” The “universal protocol” took three years to produce and was published in 2021. This article will overview (i) the evolution of the PEACE method, (ii) some of the research on effectiveness of aspects of the PEACE method, and (iii) the 2021 publication called Principles of Effective Interviewing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".