Sanctuary Politics and the Borders of the Demos: A Comparison of Human and Nonhuman Animal Sanctuaries
Bibliographic record
Abstract
Sanctuary traditionally meant something different for humans and nonhuman animals, but this is changing. Animals are increasingly seen as subjects, and, similar to human sanctuaries, animal sanctuaries are increasingly understood as political spaces. In this article I compare human and nonhuman sanctuaries in order to bring into focus under- lying patterns of political inclusion and exclusion. By investigating parallels and differ- ences I also aim to shed light on the role of sanctuaries in thinking about and working towards new forms of community and democratic interaction, focusing specifically on the role of political agency and voice. I begin by briefly discussing the political turn in animal philosophy, in which nonhuman animals are conceptualized as political actors. I then discuss ‘Zatopia’, a thought experiment that shows that viewing sanctuaries as separate from larger political structures runs the risk of repeating violence, and I investigate parallels with certain practices and policies in farmed animal sanctuaries. In order to overcome the obstacles thus identified, I turn to the concept ‘expanded sanctuary’, which explicitly focuses on connections between sanctuary and larger political structures. I discuss two examples of expanded sanctuary in which the agency and voices of those seeking or taking sanctuary are foregrounded: VINE Sanctuary, and the Dutch migrant collective WE ARE HERE. In the final section I briefly touch upon the consequences of these con- siderations for our understanding of sanctuary in relation to political membership and reforming communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".