LA METHODE D’INTERROGATOIRE « REID » ET LA METHODE POLICIERE D’ENQUETE « MR BIG » : LES ERREURS JUDICIAIRES ET L’ECART ENTRE LA VERITE ET LE MENSONGE
Bibliographic record
Abstract
Three fundamental errors can lead to a false confession and ultimately end in a wrongful conviction: misclassification, coercion, and contamination. In Canada, police officers are trained on the Reid Technique; a controversial interrogation technique that has raised concerns since it may cause false confessions due to its structure. Another police method widely used in Canada is the “Mr Big” undercover operation. Due to its nature and its potential for misuse, this undercover operation has also been criticized on the basis that it may lead to false confessions. This article aims to raise awareness on the dangers of these two police methods by examining each one and then drawing a parallel between them in order to illustrate the way both methods make the same three fundamental errors in similar ways; errors that may send an innocent to prison for a long time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.158 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".