Domestic violence fatality reviews teams: Collaborative efforts to prevent intimate partner femicide (Doctoral Dissertation). Retrieved from http://www.learningtoendabuse.ca/sites/default/files/Watt_Kelly.pdf Websdale
Bibliographic record
Abstract
Intimate partner femicide, the murder of a woman by her current or former partner, is a serious international problem. Given the gravity of intimate partner femicides, domestic violence fatality review teams have emerged in the North America as collaborative settings aimed at understanding and preventing them. Although domestic violence fatality review teams have been developed rapidly and widely, little is known about the nature of these teams or whether and how these teams actually prevent intimate partner femicide. The goals of this study were to: (1) describe the goals, structures, processes and outcomes of domestic violence fatality review teams; and, (2) identify the critical tensions or issues navigated by these collaborative efforts. The study consisted of three phases. The first phase involved a review relevant literature, discussion with experts in the field, and anecdotal experiences of team members. The second phase involved in-depth interviews with key informants and review of the most recent reports from 35 teams in the United States and Canada to gain a systematic understanding of them. At least one team was recruited from every state or province in which teams were active. Data were analyzed using frequency and content analysis. The third phase involved the use of case study
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".