Anti-Carceral Approaches to Addressing Harms Against Animals: Considerations on Multispecies Restorative and Transformative Justice
Bibliographic record
Abstract
The animal protection movement has developed an increasingly close working relationship with the criminal punishment system through lobbying and campaigning for harsher punishments for animal abuse, while at the same time showing an interest in restorative justice (RJ) as a response to harm against animals. In this article, we take a critical position aligned with anti-carceral feminists and prison abolitionists against the carceral systems that fail humans and animals in circumstances of violence. We consider the potential of RJ as an alternative approach to address and prevent harm against animals in abuse cases on an individual level while highlighting the limitations of RJ in achieving the necessary changes on a societal level to end structurally produced violence against animals, such as industrial animal exploitation. We propose that transformative justice (TJ), which involves some RJ processes, is the most promising approach that could achieve justice for both humans and nonhumans in the long term without reproducing traumas and violence for the individuals and communities involved in harm reduction and prevention. Drawing on examples of RJ and TJ as developed and practised in marginalized human communities, we apply their lessons to thinking through similar practices in the context of animal abuse and neglect.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.016 | 0.140 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".