“I FRICKEN LOVED THEM”: UNDERSTANDING THE IMPORTANCE OF ANIMALS WITHIN THE LIVES OF WOMEN SEEKING HELP FOR RURAL INTIMATE PARTNER VIOLENCE
Bibliographic record
Abstract
Saskatchewan’s high rates of intimate partner violence (IPV) has been well documented within government statistics. Research has demonstrated women who experience intimate partner violence and have a relationship with an animal, care deeply for their animal’s well-being and often have animal safety concerns. This human-animal bond provides a safe relationship within their lives and provides a source of comfort during times of stress. However, most intimate partner violence research has been largely urban centric. As a result, limited research has focused on women in rural areas who are seeking help for intimate partner violence while also having animal safekeeping concerns. \nUsing constructivist grounded theory and an anti-oppressive feminist framework, this study applied a qualitative methodology using a thematic analysis to understand who experienced rural IPV understand the roles (if any) that animals, pet, or farm, play for rural women when seeking help for IPV. \nA secondary analysis was performed on two focus groups with service providers throughout Saskatchewan and 10 interviews with women who have lived experience with rural intimate partner violence. Connections were made between what service providers were seeing at their level and the experiences shared by women. It was found that women view their animals as an important support within their lives and there is further need for animal inclusive supports for those seeking help with animal care concerns. This study highlights areas for future research and policy changes to help promote the inclusion of the human-animal bond within intimate partner violence services in the province.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".