Blurred Lines: A Critical Examination of the Use of Police Officers and Police Employees as Expert Witnesses in Criminal Trials
Bibliographic record
Abstract
There is a two-step inquiry in determining whether expert opinion evidence is admissible. The party calling the evidence must first satisfy the threshold requirements of admissibility, demonstrating that the expert evidence is relevant, necessary, not precluded by any exclusionary rule, and that it is provided by a properly qualified expert. If this threshold stage is satisfied, the court progresses to the second stage, the discretionary gatekeeping step, wherein the trial judge assesses whether the expert evidence is sufficiently beneficial to justify admission, meaning that the benefits flowing from admission outweigh any potential harm. The Supreme Court of Canada has clarified that experts must be impartial, independent, and unbiased. These factors must be considered at both steps of determining the admissibility of expert evidence and are also relevant to the determination by the trier of fact as to how much weight should be placed upon admissible expert testimony. That there are three potential points in the trial process at which expert objectivity is considered underscores the importance of ensuring that expert evidence is impartial, independent, and free of bias. This paper analyzes recent Canadian case law in relation to the use of expert witnesses and determines that structure-related concerns ultimately pertaining to bias have played a significant role in court determinations as to the admissibility of expert evidence. Guided by this finding, the authors propose a new two-stream expert structure in order to present a model for proactively reducing concerns relating to impartiality, independence, and bias about experts called by the Crown
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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 | 0.144 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.025 | 0.046 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.018 |
| 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".