Maneesha Deckha, Animals as Legal Beings: Contesting Anthropocentric Legal Orders (2021, University of Toronto Press)
Bibliographic record
Abstract
Reviewed by Rimona AfanaThe legal status of nonhumans has been hotly debated in academia, activism, and in courts over the past decades.Arguments have oscillated primarily between the property and personhood approaches (and their associated welfarist and abolitionist drives), which both come with ethical, legal, and practical challenges.Animals as Legal Beings introduces a different approach, centered on beingness, opposing the use and abuse which accompany the property approach while also transcending the anthropocentrism marking personhood."Beingness thus offers a theoretical innovation as well as a practical solution to evading the impasse that currently encapsulates the core debate in animal law circles." 1 To sketch beingness as a new legal category, Maneesha Deckha draws on feminist animal care theory, postcolonial feminist scholarship, and critical animal studies.While scholars across different disciplines informed the book, the works of Carol Adams, Josephine Donovan and Gary Francione are specified as foundational to the analysis.Animals as Legal Beings seeks "a new, transformative legal status or subjectivity" 2 for animals, by integrating insights from critical theory into animal law, which remains dominated by a liberal, anthropocentric ethos.At the heart of Deckha's project is a sentiment shared by many critical legal scholars: the law, particularly legal systems rooted in the Western liberal humanist tradition, is shaped by hierarchies when it comes to class, gender, ability, race, species, and other identity markers.The sociolegal treatment of these differences-turned-hierarchies generates structures of power, which in turn lead to violence, which can only be properly understood intersectionally.The condition of nonhumans and our fight for their rights are contextualized by Deckha within these multi-layered biological, cultural, political, legal dynamics.Thus, 1 Maneesha Deckha, Animals as Legal Beings: Contesting Anthropocentric Legal Orders (University of Toronto Press 2021) 9. 2 Ibid 6.concepts such as human, animal, animality or personhood need to be situated within the constraints of the liberal legal tradition.The deficiency of current animal laws is not the starting point of the analysis here; the law itself is.Deckha challenges the paradigmatic subject in liberal legalism: an independent, autonomous, rational, disembodied (in the sense that the fragility of our corporeality is not factored in) individual.This observation has been previously made by other theorists: I will only mention here the pioneering work of Martha Fineman, with whose Vulnerability Initiative I was previously affiliated.Fineman's vulnerability theory 3 , applied to a range of sociolegal issues, replaces this ideal(ized) legal subject with a version much closer to the reality of our human condition: embodied rather than disembodied, dependent and interdependent rather than autonomous, and vulnerable throughout our life span.Deckha embraces a similar notion of the actual (not ideal) legal subject when it comes to nonhumans.This approach is not new: over a decade ago, Ani Satz theorized animals as vulnerable subjects.4 Part 1, "Beyond Property and Personhood: Contesting Legal Objectification and Humanization" reviews the property/personhood debate, highlighting the shortcomings of both approaches.Part 2, "Animals as Beings: In Pursuit of a New Post-Anthropocentric Legal Order" posits beingness as an alternative legal category.Chapter 1, "No Escape: Anti-cruelty Laws' Property Foundations", covers the welfarist approach in Canada, similar to that in other legal systems rooted in British common law, which sustain human exceptionalism.Looking at Canada's Criminal Code and at a range of cases showing how anti-cruelty laws have been applied, Deckha concludes that animal protection laws rooted in the property approach are self-defeating, as they cannot adequately protect animals.Cruelty is framed in a narrow way, typically limited to the most severe of cases and usually victimizing companion animals.Cruelty routine in organized forms of animal (ab)use (sectors like food, clothing, research, 3 Martha A. Fineman, The Autonomy Myth: A Theory of Dependency (The New Press 2004); Martha
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".