Women’s stories of non-fatal strangulation: Informing the criminal justice response
Bibliographic record
Abstract
Non-fatal strangulation is commonly reported by women who have experienced intimate partner violence and it has been identified as both an immediate risk to health and life but also a risk for future serious harm and even death. While some Australian states and Canada have followed the lead of American states in introducing criminal offences of non-fatal strangulation the United Kingdom is yet to do so. Non-fatal strangulation offences have come with challenges of definition and identification. The success of criminal justice responses requires an understanding of the ways in which women understand and describe their non-fatal strangulation victimisation. We analyse 24 women’s experiences of non-fatal strangulation as a basis for considering how to ensure that jurisdictions considering introduction of a new non-fatal strangulation offence or reform of an existing offence do not reproduce obstacles to prosecution and legal recognition and suggest a model definition of non-fatal strangulation for an offence.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".