Bibliographic record
Abstract
Recent studies have found high rates of coexistence between animal abuse and other forms of family violence. This study explores the resultant questions of how and why animal abuse and other forms of family violence frequently coexist. In an effort to address these questions, information was gathered through in-depth, semi-standardized interviews with abused women who had at least one pet while they were with their abusive partner. In particular, this study focuses on the women's experiences and interpretations of how and why these forms of abuse coexist, and the degree to which the animal abuse perpetrated by their partners was instrumental or expressive. It is demonstrated that animal abuse was predominantly instrumentalized by the participants' abusive partners to gain power and control over them and their children, and it was additionally perpetrated out of jealousy in cases where the pet posed a threat to the attention and devotion the abuser received from his partner. Recommendations are made in light of the research findings, and further research in this area, and human-animal relations in general, is urged.Dept. of Sociology and Anthropology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .F53. Source: Masters Abstracts International, Volume: 40-06, page: 1430. Adviser: Tanya Basok. Thesis (M.A.)--University of Windsor (Canada), 2001.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".