Sensitizing the general public: experiential processing alters perceptions of confession evidence and minimization
Bibliographic record
Abstract
False confessions are a leading cause of wrongful convictions in Canada, suggesting a lack of protection against false confessions. Moreover, minimization tactics can increase the chance of false confessions. Current educational efforts (i.e., cautionary instructions and expert testimony) do not sufficiently sensitize jurors to false confessions, increasing the chance that false confessions will result in a wrongful conviction. We hypothesized that experiential processing (via personal learning) would alter perceptions of confessions. In Study 1, participants read vignettes whereby minimization led to a confession, and provided perceptions of the confession. Veracity of the confession was manipulated. Learning that a seemingly appropriate interrogation lead to a false confession altered perceptions of that confession evidence. This change in perception carried over to a second, unrelated case. In Study 2, we replicated these findings using video clips of actual Canadian interrogations, strengthening ecological validity of the study. Implications for jury decision making are discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".