‘Then what happened?’: an analysis of victim and witness interview training and practices in Nunavut
Bibliographic record
Abstract
A survey of police officers in the Canadian Territory of Nunavut and a content analysis of 20 victim/witness interviews revealed that although police officers are aware of the difficulties of interviewing and can describe some elements of a competent interview, their actual practices are similar to those found in other studies: less than ideal. Interviewers violated the 80/20 talking rule in most of the interviews, and used more unproductive questions than necessary for cooperative interviewees. Although full accounts were requested in all interviews, and officers demonstrated good listening skills, the questioning sequences that followed the free recall showed an inappropriate level of control, with a pattern of asking alternating inappropriate closed or clarifying questions, and probing questions, in an attempt to pin the interviewee down to a story acceptable to the interviewer. In addition, although some officers could explain certain Cognitive Interview components in the survey, few attempts were made to incorporate Cognitive Interview techniques. Training was found to be mostly suspect interview-based, with officers having taken various courses or only the training offered in recruit basic training. The author recommends that victim and witness interview training for police officers in the Canadian north take into account the characteristics of Inuit culture, which is collectivist, high-context and polychromic. Training should also be standardised, with a tier-based training protocol such as PEACE, being implemented in Nunavut.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".