Bibliographic record
Abstract
Abstract In spite of widespread statutory reform to rape and sexual assault laws within Canada, the United Kingdom, the United States, and other legal systems derived from the British common law system, many feminist scholars and practitioners have lamented the failure of sexual assault and rape cases to live up to their statutory ideals. A similar situation exists with respect to domestic violence law reform: while domestic violence has been criminalized in many common law countries over the last number of decades, there are serious misunderstandings among police officers, lawyers, and judges about the complexities of relationships involving domestic violence. In this entry, then, my focus is not so much on feminist‐inspired statutory reform to gendered violence statutes, but rather on the linguistic and discursive dimensions of legal practices and processes that have contributed to the “nullifying” of that reform. Indeed, as this entry shows, there is much research to suggest that when women recount their experiences of sexual and/or domestic violence to paralegals, in police interviews or in courtrooms and/or when these recountings are transplanted into other sites within the legal system, such representations are (re)shaped by legal structures and categories and by problematic cultural ideologies of gender and sexuality.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".