Multispecies Disposability: Taxonomies of Power in a Global Pandemic
Bibliographic record
Abstract
This paper bridges critical conversations regarding animal exploitation and racialized violence that have been occurring throughout the COVID-19 pandemic. We apply Claire Jean Kim’s analysis of taxonomies of power to help make sense of the interwoven multispecies catastrophes of racialized animalization and animalized racialization, such as the violence experienced by various species of nonhuman animals, as well as East Asians and other People of Colour in the West, whether in public spaces, in media, on farms, or inside industrial animal slaughterhouses or meatpacking plants. We conclude by arguing that Kim’s ethics of mutual avowal provides a productive way for social movements to recognize the connectedness of our struggles; thus, Kim’s scholarship helps us challenge the multiple dimensions of oppressive powers that have been expressed and experienced in the COVID-19 crisis.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".