Pierson v. Post: A Great Debate, James Kent, and the Project of Building a Learned Law for New York State
Bibliographic record
Abstract
Pierson v. Post(1805) has long puzzled legal teachers and scholars. This article argues that the appellate report was the product of the intellectual interests (and schooling) of the lawyers and judges involved in the case. They converted a minor dispute about a fox into a major argument in order to argue from Roman and other civil law sources on how to establish possession in wild animals, effectively crafting an opportunity to create new law for New York State. This article explores the possibility that the mastermind behind this case was the chief justice of the court at the time, James Kent. The question of Kent's involvement in 1805 remains elusive. However, the article uses annotations he made on his copy of the case and discussion ofPierson v. Postin his famousCommentariesto demonstrate the nature of his later interest and to explore the project of building a learned law for New York State.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".