Animal Bodies, Colonial Subjects: (Re)Locating Animality in Decolonial Thought
Bibliographic record
Abstract
In this paper, I argue that animal domestication, speciesism, and other modern human-animal interactions in North America are possible because of and through the erasure of Indigenous bodies and the emptying of Indigenous lands for settler-colonial expansion. That is, we cannot address animal oppression or talk about animal liberation without naming and subsequently dismantling settler colonialism and white supremacy as political machinations that require the simultaneous exploitation and/or erasure of animal and Indigenous bodies. I begin by re-framing animality as a politics of space to suggest that animal bodies are made intelligible in the settler imagination on stolen, colonized, and re-settled Indigenous lands. Thinking through Andrea Smith’s logics of white supremacy, I then re-center anthropocentrism as a racialized and speciesist site of settler coloniality to re-orient decolonial thought toward animality. To critique the ways in which Indigenous bodies and epistemologies are at stake in neoliberal re-figurings of animals as settler citizens, I reject the colonial politics of recognition developed in Sue Donaldson and Will Kymlicka’s recent monograph, Zoopolis: A Political Theory of Animal Rights (Oxford University Press 2011) because it militarizes settler-colonial infrastructures of subjecthood and governmentality. I then propose a decolonized animal ethic that finds legitimacy in Indigenous cosmologies to argue that decolonization can only be reified through a totalizing disruption of those power apparatuses (i.e., settler colonialism, anthropocentrism, white supremacy, and neoliberal pluralism) that lend the settler state sovereignty, normalcy, and futurity insofar as animality is a settler-colonial particularity.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.107 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".