Bibliographic record
Abstract
This book explores the ways that international politics is a form of interspecies politics, one that involves the interactions, ideas, and practices of multiple species, both human and nonhuman, to generate differences and create commonalities. While we frequently think of having an international politics "of" the environment, a deep and thoroughgoing anthropocentrism guides our idea of what political life can be, which prevents us from thinking about a politics "with" the environment. This anthropocentric assumption about politics drives both ecological degradation and deep forms of interhuman injustice and hierarchy. Interspecies Politics challenges that assumption, arguing that a truly ecological account of interstate life requires us to think about politics as an activity that crosses species lines. It therefore explores a postanthropocentric account of international politics, focusing on a series of cases and interspecies practices in the American borderlands, ranging from the US-Mexico border in southern Texas, to Guantánamo Bay in Cuba, to Isle Royale, near the US-Canadian border. The book draws on international relations, environmental political theory, anthropology, and animal studies, to show how key international dimensions of states—sovereignty, territory, security, rights—are better understood as forms of interspecies assemblage that both generate new forms of multispecies inclusion, and structure forms of violence and hierarchy against human and nonhuman alike.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".