Bibliographic record
Abstract
This chapter begins by pointing out some of the shortcomings of the present literature on women who kill. Most of the research addressing the issue of women who kill has emphasised primarily statistical differences between male and female defendants’ circumstances in homicides. Whilst these studies do offer general patterns and trends of homicides between partners, their research is limited by the lack of a detailed, indepth analysis. I argue that the absence of an examination of homicide documents will continue to hinder discussions about the legal status of women murderers. Following this is a discussion of the methodology and methodological issues involved in the study that is the concern of this book. I demonstrate how the use of homicide documents can overcome some of the problems identified in the present literature. As well, I point to some of the strengths and weaknesses associated with using documents. I then discuss in detail the process of obtaining access to the documents, as well as the selection and analysis of the homicide cases. Finally, I offer some basic statistics of the homicide cases in this study to provide a general overview for the analysis in the chapters to follow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".