THE JUDICIAL INVENTION OF PROPERTY NORMS: ELLICKSON’S WHALEMEN REVISITED
Bibliographic record
Abstract
Robert C Ellickson argues that the close-knit community of American whalemen in the nineteenth century used norms of their own creation to settle arguments over contested whales without violence or frequent litigation. Ellickson also contends that these norms were largely adopted by courts as the property law of whaling. A close examination of trial transcripts and depositions from two litigated whaling disputes reveals, however, that whalemen settled contests not upon clear and widely accepted norms but rather upon the application of some rather general maxims that were often poorly understood even by experienced captains and crews. Whaling disputes were, in fact, most often settled through compromises grounded in inchoate notions of what constituted honourable behaviour arising out of the particular situation and parties involved. In seeking to settle the handful of litigated disputes, judges drafted opinions that suggested a level of agreement among whalemen as to prevailing norms that never existed at sea. The scholarly acceptance that judges accurately stated whaling customs explains the mistaken belief that whalemen created the American property law of whaling. Instead, judges and the lawyers who represented ship owners were, to a large degree, responsible for creating much of what came to be memorialized in legal treatises by the end of the nineteenth century as the property law of whaling.
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.055 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.027 | 0.022 |
| 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".