The Reid Inter rogation Technique and False Confessions: A Time for Change
Bibliographic record
Abstract
The Reid Interrogation technique has been the dominant method used by police in the United States and Canada to interview suspects of crime. This method is commercially marketed to police departments and other law enforcement agencies with the promise that 80 percent of those interrogated will confess. However, there is growing evidence that the Reid technique results in a significant number of false confessions, especially among the young, the mentally impaired and those of low intelligence. Other countries, especially England have rejected the Reid technique in favor of other methods that work equally well in obtaining confessions but without the risk of false confessions. In the United States, too, there is growing suspicion of the Reid technique and other hard interrogation tactics such as those employed in interrogating suspected terrorists at Guantanamo and Abu Ghraib.\nThis paper suggests that widespread use of the Reid technique is a significant contributing factor in public distrust of the police, and fosters police attitudes that feed that dissatisfaction. Rejection of the Reid technique in favor of other methods is likely to improve police efficiency as well as help heal the growing rift between police personnel and the communities they serve.
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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 | 0.080 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.027 | 0.074 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.036 | 0.047 |
| Insufficient payload (model declined to judge) | 0.009 | 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".