Challenges of a “toolbox” approach to investigative interviewing: A critical analysis of the Royal Canadian Mounted Police’s (RCMP) Phased Interview Model.
Bibliographic record
Abstract
The Royal Canadian Mounted Police implemented the Phased Interview Model in Canada and has argued that it is a novel and productive way to interview suspects.We applaud the Royal Canadian Mounted Police for moving away from a purely accusatorial approach and recognize that Phased Interview Model contains several science-based practices.In this article, however, we evaluate the Phased Interview Model critically.In particular, we present compelling empirical evidence that three fundamental practices (minimizing culpability, mischaracterizing evidence, and asking leading questions) in the Phased Interview Model put the truth-seeking function of police interviews at risk.We also explore the challenges inherent in combining accusatorial and information gathering techniques into a hybrid 'toolbox' approach.We conclude that advocating for interview protocols that contain dangerous or untested practices may hinder the Royal Canadian Mounted Police's ability to achieve their purported goals of obtaining voluntary statements and accurate information.
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 | 0.235 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.027 | 0.081 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".