Teaching Sexual Assault: The Education of Canadian Law Students
Bibliographic record
Abstract
In this article the authors discuss how sexual assault is taught to first-year law students, particularly in criminal law. This work is a continuation of a collaborative, non-traditional first-year criminal law course that specifically targeted students interested in social justice (see “Resisting the Hidden Curriculum: Teaching for Social Justice,” available on SSRN at http://ssrn.com/abstract=2261882). The authors begin with a discussion of the social and political significance of legal education in addressing sexual assault. Based on an informal survey of legal educators across the country, five key issues related to teaching sexual assault arose: 1) politics embedded in sexual assault education, 2) understanding and making explicit our objectives in teaching in this area, 3) unpacking the interaction between legal doctrine and the influence of social policy on creating those doctrines, 4) personal identity of students and educators in the classroom, 5) sensitive classroom dynamics resulting from teaching sexual assault. The article concludes by connecting the author's findings to Jane Doe and her legal battle following her experience of sexual violence, reaffirming that legal students and professors have an obligation to bring critical perspectives of law into the classroom; legal education plays a vital role in transforming the legal system and dismantling institutions that enable sexual assault.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".