Intoxication, a Drunk Science: Expertise in Cases of Sexual Assault regarding Capacity to Consent
Bibliographic record
Abstract
This paper analyzes the use of expert and forensic evidence in cases of sexual assault when the complainant alleges incapacity to consent due to intoxication. Based on a review of recent jurisprudence, the following argues that despite its rampant use in sexual assault trials, expert testimony and forensic evidence are frequently unable to provide precise conclusions about a complainant’s level of intoxication and consequently capacity to consent. While trial judges continue to call on counsel to bring forth these types of evidence, they are rarely assigned probative value. Nonetheless, inconclusive expert evidence and testimony is still relied upon to advance theories which undermine the complainant’s narrative and uphold damaging stereotypes about sexually assaulted intoxicated women. While these types of evidence can contribute to the truth-seeking process, this paper calls on the legal community to critically evaluate how these scientific tools are being utilized. What voices and narratives are being amplified by expertise? Furthermore, it asks whether expertise is actually relevant and informative to the central issues alleged in cases of capacity to consent. Are expert testimony and forensic evidence truly allowing the trier of fact to get at the truth?
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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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| 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".