Bibliographic record
Abstract
This paper examines animal welfare and gender equity from a feminist public policy perspective. Utilizing a wide secondary literature from India, the USA, the UK, and Canada, we synthesize evidence that animal cruelty tends to co‐occur with violence against women, and most importantly, both phenomena emanate from similar power relations within patriarchal societies. We examine studies on how abusers use the abuse of pets as a coercive strategy within domestic violence. We also explore how often women delay leaving violent households due to fear of harm to their pets. At the same time, we show how the reproductive exploitation of female animals has much in common with women’s marginalization during reproductive drudgery. Because of gendered expectations about care, women in animal-keeping roles bear additional emotional and physical burdens. In general, we observe a tendency for males–across different species–to hurt others, which is part of the feminist critique of patriarchy for dominating women and animals. We highlight significant issues with policy, such as how domestic violence programs ignore companion animals and how animal cruelty laws lack a gendered lens. Ultimately, we advocate for integrated policy reforms, which we characterize as a “One Welfare” approach. These would link animal protection with gender equity. For example, including pets in protective orders, training first responders on “the link” and harmonizing animal welfare legislation with women’s rights. Our analysis suggests that addressing animal cruelty and gender-based violence together can strengthen protections for all vulnerable lives.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".