Bibliographic record
Abstract
As with all police organizations around the world, interviews with suspects and accused persons are integral to the investigation of crime in Canada. To gain an appreciable understanding of suspect interviewing in Canada, it is important for readers to be familiar with the structure and autonomy of Canadian policing. Although there is much autonomy for police officers to use their preferred interviewing practices, there is legislation and case law that provides all interviewers with guidance on what are acceptable and unacceptable practices during custodial and non-custodial interviews. Even though there is a growing body of empirical literature that is starting to inform interviewing practices in Canada, much of what has driven investigative interviewing is based on common-sense notions of what should work and, more recently, empirically driven guidance on what actually works. There has been a major advancement in Canada over the last five years where the scientifically driven PEACE Model of Investigative Interviewing is challenging previously cherished beliefs and practices. As with any promising development, there has been an anticipated resistance to change; much work is therefore needed to reform investigative interviewing to continue the move from coercive practices (and their seemingly less coercive hybrid derivatives) to an empirically based and ethical model. Consequently, the goal of the current chapter is to provide an overview of the aforementioned current state of affairs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.003 |
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; both teacher heads agree on what is shown here.
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".