High-risk interrogation: Using the “Mr. Big Technique” to elicit confessions.
Bibliographic record
Abstract
Kassin et al. (Police-Induced Confessions: Risk Factors and Recommendation, 2009) provide a detailed and thoughtful analysis of how police interrogation practices might elicit false confessions from innocent suspects. The purpose of this commentary is to provide a brief review of a relatively recent development in Canadian police investigation practice and discuss how this procedure may increase the likelihood of police-induced false confessions. The so-called "Mr. Big Technique" is a non-custodial interrogation tactic wherein suspects are drawn into a supposed criminal organization (actually an elaborate police sting) and subsequently told that to move up in the organization, they must confess to a crime. In this article, we describe this remarkable interrogation technique and discuss issues relevant to the potential induction of false confessions.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".