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Record W4404488462 · doi:10.1017/lsi.2024.45

Anti-Carceral Approaches to Addressing Harms Against Animals: Considerations on Multispecies Restorative and Transformative Justice

2024· article· en· W4404488462 on OpenAlexaff
Kelly Struthers Montford, Darren Chang, Selingul Yalcin

Bibliographic record

VenueLaw & Social Inquiry · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of OttawaToronto Metropolitan University
Fundersnot available
KeywordsRestorative justiceTransformative learningCriminologyEnvironmental ethicsEconomic JusticeSociologyEnvironmental planningEngineering ethicsPolitical sciencePsychologyEngineeringLawGeographyPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.504
GPT teacher head0.374
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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