Urgent issues and prospects in reforming interrogation practices in the United States and Canada
Bibliographic record
Abstract
The current article presents a series of commentaries on urgent issues and prospects in reforming interrogation practices in Canada and the United States. Researchers and practitioners, who have devoted much of their careers to the field of police and intelligence interrogations, were asked to provide their insights on an area of interrogation research that they believe requires immediate attention. The submitted independent commentaries covered a variety of topics – from police recruitment, interrogation training, use of proper interrogation practices, and the treatment of confession evidence in court. Common concerns from the contributions pertained to the lag between scientific knowledge on interrogations and the application of such knowledge in the justice system, and the glaring disparity between the treatment of similar issues in the interrogation context versus other criminal justice contexts. A primary intent of this collection of commentaries is to serve as a resource pointing researchers in the direction of the fundamental areas that require immediate consideration and encouraging them to simultaneously pursue solutions to the overarching concerns that emerged from this project.
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 | 0.056 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.055 | 0.022 |
| Scholarly communication | 0.025 | 0.006 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".