The impact of negative forensic evidence on mock jurors' perceptions of a trial of drug-facilitated sexual assault.
Bibliographic record
Abstract
Legal concerns with regard to the adverse impact of a negative toxicological screening for date-rape drugs in a case of drug-facilitated sexual assault (DFSA) were the focus of a recent Canadian case (R. v. Alouache, 2003). To assess the impact of a negative forensic report, as well as the impact of expert testimony explaining the many factors that may contribute to a negative outcome, participants (N=171) received a written trial stimulus in which the forensic evidence (negative report, negative report plus expert testimony, no negative report and no expert testimony control) and the complainant's beverage consumption (alcohol, cola) were systematically varied. Results indicate that a negative finding in the absence of expert testimony produced greater verdict leniency and more favourable evaluations of the defendant's case. In contrast, no differences were found between the case in which the expert testified and a case in which the negative report and expert testimony were omitted.
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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 | 0.010 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".