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Record W3042200976 · doi:10.3390/socsci9070122

Animals as Something More Than Mere Property: Interweaving Green Criminology and Law

2020· article· en· W3042200976 on OpenAlexaff
James Gacek, Richard Jochelson

Bibliographic record

VenueSocial Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of ManitobaUniversity of Regina
Fundersnot available
KeywordsScholarshipCrueltyAnthropocentrismProperty (philosophy)LawGreen criminologySociologyCriminal lawEconomic JusticeCriminal justiceLegislationCriminologyPower (physics)Environmental ethicsPolitical scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Our article argues that non-human animals deserve to be treated as something more than property to be abused, exploited, or expended. Such an examination lies at the heart of green criminology and law—an intersection of which we consider more thoroughly. Drawing upon our respective and collective works, we endeavor to engage in a discussion that highlights the significance of green criminology for law and suggests how law can provide opportunities to further green criminological inquiry. How the law is acutely relevant for constituting the animal goes hand in glove with how humanness and animality are embedded deeply in the construction of law and society. We contend that, when paired together, green criminology and law have the potential to reconstitute the animal as something more than mere property within law, shed light on the anthropocentric logics at play within the criminal justice system, and promote positive changes to animal cruelty legislation. Scholarship could benefit greatly from moving into new lines of inquiry that emphasize “more-than-human legalities”. Such inquiry has the power to promote the advocacy-oriented scholarship of animal rights and species justice.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.789

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.168
GPT teacher head0.339
Teacher spread0.170 · 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 designObservational
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

Citations10
Published2020
Admission routes1
Has abstractyes

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