Rethinking the Ken Through the Lens of Psychological Science
Bibliographic record
Abstract
Canadian courts regularly exclude psychological expert evidence that would explain the factors that produce mistaken eyewitness identifications and false confessions (two significant sources of wrongful convictions). 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 the judge and jury. In this article, the authors suggest this reasoning rests on two fundamental misunderstandings of psychology: unconscious neglect and dispositionism. In other words, judges mistakenly assume the trier of fact understands the unconscious situational forces that distort memories and cause innocent people to confess. Moreover, judges appear to prefer dispositional evidence of some disorder or syndrome suffered by the accused or by the witness to the crime. After demonstrating evidence of such reasoning in several decisions, the authors suggest reforms based on a more nuanced understanding of human psychology.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".