Bibliographic record
Abstract
At the intersection of ethics of care and animal geographies scholarship are important discussions about human-animal power dynamics, violence, and what it means to understand and care for animals in ethnographic research where power and violence are prevalent. In this article, I add nuance to these debates by expanding on Shotwell’s work on impurity to consider, what I call, an impure ethics of care. This, I explain, complicates and strengthens our understanding, as scholars, of our ethically fraught relations with animals and the research contexts we enter to address and respond to animal violence. I show how an impure ethics of care highlights the challenges and complexities of power, violence, care, and relationality and, most importantly, how it contributes to efforts of building more ethical relations with animals through scholarship and research practice. I ground this discussion in a study of multispecies participant observation at live cow auctions in Ontario, Canada, unpacking the violence cows endure and respond to in sale barns to which an impure ethics of care responds.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".