Keeping Pets Safe in the Context of Intimate Partner Violence
Bibliographic record
Abstract
The connection between intimate partner violence (IPV) and abuse against animals is becoming well-documented. Women consistently report that their pets have been threatened or harmed by their abuser, and many women delay leaving abusive relationships out of concern for their pets. Shelters are often faced with limited resources, and it can be difficult to see how their mandate to assist women fleeing IPV also includes assistance to their companion animals. Through surveys with staff from 17 IPV shelters in Canada, the current study captures a snapshot of the shelter policies and practices regarding companion animals. The study explores staff’s own relationships with pets and exposure to animal abuse, as well as how these experiences relate to support for pet safekeeping programs, perceived barriers, and perceived benefits for the programs. Policy implications for IPV service agencies include asking clients about concerns about pet safety, clear communication of agency policies regarding services available for pet safekeeping, and starting a conversation at the agency level on how to establish a pet safekeeping program in order to better meet the needs of women seeking refuge from IPV.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".