An Archival Analysis of Actual Cases of Historic Child Sexual Abuse: A Comparison of Jury and Bench Trials.
Bibliographic record
Abstract
Logistic regression analyses were used to predict verdicts from 466 Canadian jury and 644 Canadian judge-alone criminal trials involving delayed or historic allegations of child sexual abuse. Variables in regard to the complainant and offence were selected from the legal, clinical, and experimental literatures, including mock juror research. Of six variables that had been related to decisions reached in mock juror research concerning delayed allegations of child sexual abuse (i.e., repressed memory testimony, involvement in therapy, length of delay, age of complainant, presence of experts, and frequency of abuse) two (age of complainant and presence of expert) predicted verdicts. An additional five variables (duration, severity, complainant-accused relationship, threats, and complainant gender) were also examined: of these, threats and the complainant-accused relationship reliably predicted jury verdicts. For judge-alone trials, five variables predicted verdict: length of the delay, offence severity, claims of repression, the relationship between complainant and accused, and presence of an expert. Implications of the jurors' and judges' differential sensitivity to these variables for future simulation and archival research 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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".