Fuzzy rules and clear enough standards: The Uses and Abuses of <i>Pierson v Post</i>
Bibliographic record
Abstract
<i>Pierson v Post</i>, the famous fox case, has come to be understood by law and economics scholars as a parsed down lesson about rules versus standards, specifically the superiority of the clear capture rule over the allegedly fuzzy standard of hot pursuit articulated by Justice Livingston in his dissent. This article argues first, that the case actually does not illustrate very well the superiority of rules over standards. And second, that, even if it did, scholars who look at the case in this way are missing something very important; namely, the tongue-in-cheek style of Livingston’s dissent, which if taken completely seriously will lead one astray. The article traces the process of the serious ‘mandarization’ of the case from James Kent in the 1820s to Oliver Wendell Holmes, Jr, in the later nineteenth century. It then shows how that serious treatment continued in the twentieth century. This survey of the uses (and abuses) of the case will be of interest to those who read legal history, legal pedagogy, legal theory, and property law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".