The Moral Paradox of Adverse Possession: Sovereignty and Revolution in Property Law
Bibliographic record
Abstract
On what grounds can we justify the transformation of squatters into owners? To understand the moral significance of adverse possession, the author proposes an analogy. Much of the moral analysis of adverse possession has proceeded on the basis that adverse possessors are land thieves. The author first explains why the analogy of adverse possessor to land thief is misleading. Then, she argues that there is a much closer analogy between adverse possession and revolution or, more precisely, a bloodless coup d’état. The recognition of the adverse possessor’s (private) authority solves the moral problem created by an agendaless object just as the recognition of the existing government’s (public) authority, whatever its origin, solves the moral problem of a stateless people. The morality of adverse possession, seen this way, does not turn on any particularized evaluation of the squatter’s deserts or her uses of the land. The author thus does not propose that adverse possession is justified in the same way that some argue a conscientious revolutionary is justified in resisting an oppressive or otherwise unjust sovereign. Rather, the morality of adverse possession is found where we might least expect it: in its positivist strategy of ratifying the claims to authority of a squatter without regard to the substantive merits of her agenda or her personal virtue.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".