The “Bundle” or “Cluster” Theory of Legal Personhood in Its Active and Passive “Incidents”: What Might It Mean for Nonhuman Animals?
Bibliographic record
Abstract
Abstract In this article, I review A Theory of Legal Personhood, explaining what I see as its key contributions to animal law scholarship, while situating it against wider jurisprudential contributions that may be of interest to philosophers and legal scholars grappling with the oft-thorny idea of legal personhood, not just for nonhuman animals but for corporations, artificially intelligent machines, and late-term fetuses. The article will explain Kurki's “bundle” theory of legal personhood as a “cluster” concept and analyze the extremely helpful parsing his theory provides in terms of the active and passive “incidents” of legal personhood. I focus much of the piece on Kurki's view of legal “nonpersons” who nonetheless have some rights or incidents of personhood in order to help clarify the challenge Kurki's theory raises for Steven M. Wise and the Nonhuman Rights Project, as the issues surrounding those litigation efforts will likely be familiar to readers here, who will be wondering how this theory interacts with what Wise seeks to achieve.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".