Bibliographic record
Abstract
Abstract In this article, we argue that the phenomenon of predation is the source of several problems for Donaldson and Kymlicka's account of our duties towards wild and liminal animals. According to them, humans should adopt a general policy of non‐intervention with respect to predatory behaviour involving wild and liminal animals. They justify this recommendation by appealing to the status of those animals as, respectively, members of sovereign communities and denizens of human‐animal societies. Our goal is not to question their recommendation, but to challenge the reasons given in its support. On the one hand, we argue that, insofar as wild animal communities are incapable of dealing with massive predation, they do not possess the competence required for sovereignty. Moreover, we argue that, even if we leave the issue of competence aside, attributing sovereignty rights to communities including both predators and preys may not be the best way to protect wild animals’ fundamental interests. On the other hand, we argue that there exist two important disanalogies between human denizens and liminal animals, which render Donaldson and Kymlicka's denizenship framework problematic. We suggest that the ultimate justification for a general policy of non‐intervention lies in the significant risk of causing greater harm by acting otherwise, due to our limited knowledge and resources.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".