Bibliographic record
Abstract
This book presents a critical evaluation of the "Mr.Big" investigative technique, a tool used by the Royal Canadian Mounted Police (RCMP) for obtaining incriminating evidence from key suspects in murder investigations.Easily located in legal, sociological, and criminological research on the topic of wrongful conviction, the book would be most useful to those with an academic background or interest in the social sciences, particularly in the disciplines of criminology and criminal justice, law and legal studies, and sociology.Although the book is written plainly enough for engagement and understanding by a reader of any discipline, some of the legal and other specialized jargon might limit the reach of this critical evaluation of investigative practices in the Canadian criminal justice system.Keenan and Brockman do three things: 1) they present the reader with a thorough account of the Mr. Big investigative technique, its prominence, and its general script; 2) they relate the tactics employed during Mr. Big operations to the rate and risk of erroneous convictions; and 3) they draw on a number of procedural, legal, ethical, and moral issues that arise through its use.The study draws on 81 documented Canadian Mr. Big operations and relevant journalistic reports as their supplements, analyzing transcripts, court documents, and journalistic reports through coding and content analysis.The authors begin by providing an effective illustration of the Mr. Big investigative technique by detailing three notorious operations, with varying outcomes, in order to provide the reader with an understanding of this increasingly common police tactic.The Mr. Big investigative technique is a tool employed by the RCMP to elicit incriminating information from targets -suspects in criminal investigations.Generally, the technique follows a standard script, staged by undercover officers who 1 Allison Wallis is a Master's candidate in the Department of Sociology and Anthropology at Carleton University.Her current research focuses on the embodiment and performativity of animal rights by self-described animal rights activists within her community.She can be reached at allison_wallis@carleton.ca
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 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".