Rethinking the Ken Through the Lens of Psychological Science
Bibliographic record
Abstract
Canadian courts are loathe to admit expert evidence from psychological scientists when that evidence does not concern a disposition, typically a psychological disorder. As a result, psychological evidence concerning the unconscious processes and situational forces that underlie mistaken eyewitness identifications and wrongful confessions are regularly excluded from courtrooms. Courts justify these exclusions on the basis that the evidence is not beyond the ken of the trier of fact – the psychologist would simply be describing an experience shared by judge and jury. This reasoning stands on a fundamental misunderstanding of psychology. In fact, psychological science finds that the situation drives behaviour in a manner that regularly evades the trier of fact’s ken. This is because these situational forces rely on unconscious cognitive processes, and humans rarely have introspective access to these processes. As a result, humans cling to several deep misconceptions about memory processes and confessions. In this Article, we first discuss the mechanics of the mischief: why humans fail to account for unconscious processes and the situation, and instead focus on disposition. Then we provide evidence for this type of reasoning in decisions to exclude expert evidence about eyewitness identifications and false confessions. We conclude with recommendations to manage prejudicial evidence in a manner that is based on a more nuanced understanding of human psychology.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.007 | 0.102 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".