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Record W2999485323 · doi:10.1080/07256868.2019.1704229

Unsettling Anthropocentric Legal Systems: Reconciliation, Indigenous Laws, and Animal Personhood

2020· article· en· W2999485323 on OpenAlexaffabout
Maneesha Deckha

Bibliographic record

VenueJournal of Intercultural Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAnthropocentrismPersonhoodIndigenousEnvironmental ethicsSociologySubjectivityArgument (complex analysis)LawPolitical scienceEpistemologyEcologyBiology

Abstract

fetched live from OpenAlex

This paper argues that interspecies justice is integral to rising decolonizing nationalist ‘reconciliation’ efforts in Canada and that such an interspecies perspective on reconciliation carries a significant promise for developing a new legal subjectivity for animals in settler colonial law to change the conditions of the lives of animals materially. I demonstrate that the personhood ascribed to animals in numerous Indigenous legal orders in Canada, as well as underlying non-anthropocentric worldviews where animals are not considered inferior to humans but are to be regarded as kin, should stimulate a new legal conversation in Canadian law about who/what animals are and the legal subjectivity and regard they merit among all those committed to reconciliation. Indigenous legal orders offer animal advocates a new and potentially transformative legal argument as to why the continued legal classification of animals as a property in Canadian law is exploitative and incompatible with a dominant legal order seeking to foster genuine reconciliation. Notwithstanding the residual anthropocentric elements of Indigenous worldviews promoting ‘respectful’ or ‘reciprocal’ relations with animals, and how such elements might be co-opted by settler society, this new reconciliation-originating animal-friendly argument has the potential, if adopted, to alter the material conditions of lives of many animals, most notably in intensive agriculture.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.100
Scholarly communication0.0090.005
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.352
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2020
Admission routes2
Has abstractyes

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