Unsettling Anthropocentric Legal Systems: Reconciliation, Indigenous Laws, and Animal Personhood
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.100 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".