Bibliographic record
Abstract
Traditionally, the act of murder has been viewed as a crime committed by men. The subject of women’s violent crimes has received only cursory attention in past academic research, and media attention typically focuses on high-profile or sensational cases such as those of Sara Thornton and Kiranjit Ahluwahlia. As a result, there has been a tendency in discussions about women’s acts of murder to view their crimes as an aberration or to stereotype women who kill as inherently evil. In doing so, they are no longer viewed as ordinary women, but are now categorized as violent offenders. This has led to allegations of discriminatory treatment of women murderers in the legal system. Whilst scattered non-feminist research has been conducted in this area, it has not offered an analysis of the treatment of women murderers in the criminal justice system. It was not until the mid-1970s that feminists’ research in this area became significant and began to address the issue of legal discrimination against female homicide defendants. Yet, in an attempt to understand the actions and motivations of women in England who kill, writers have had to rely on the few feminist studies emanating primarily from America and Australia. The absence of a detailed analysis highlighting the circumstances of women who kill their partners in England has arguably hindered the debate about the legal treatment of women murderers in England.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".