Sense and Sensibilities: A Feminist Critique of Legal Interventions against Sexual Violence
Bibliographic record
Abstract
Feminists have spent decades trying to reform laws and evidential procedures relating to sexual assault. Using the current Scottish context as a case study, I will argue that while efforts to reform the text of the substantive as well as evidential and procedural aspects of the law have been largely successful, in practice the impact of these reforms has not always been felt. Drawing on contemporary examples from Scotland, and setting these within the broader context of similar problems and arguments in other jurisdictions such as England and Wales, Australia and Canada, I will examine the ways in which the ‘laws on the books’ have not always translated smoothly through to ‘law in action’. The aim is to highlight an ongoing failure on the part of those charged with applying the law (judges, legal professionals, juries) to do so appropriately, raising the question of whether it makes sense for feminist scholars to try to engage with what seems like the entrenched ‘sensibilities’ of criminal law. It may well be that the contemporary battle ground is not over legal territory as such, but over whose voices are heard in public debates on sexual violence. Ultimately, I argue that our all too frequent failures to punish sexual violence in a meaningful way suggests that we need to think again about how we deal with issues of sexual violence in contemporary society.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.012 | 0.122 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| 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".