The impact of negative forensic evidence on mock jurors' perceptions of a trial of drug-facilitated sexual assault.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it