Bibliographic record
Abstract
Holmes is an irresistible figure for those of us with economics in our quiver, uttering as he does such sweet nothings as these: “For the rational study of law the black-letter man may be the man of the present but the man of the future is the man of statistics and the master of economics.” Holmes was remarkably prescient in forecasting the ever-widening use of economic analysis in the study of the law, a phenomenon that has been well entrenched now for a quarter of the century that has passed since The Path of the Law was published. In his thoughtful essay, “The Path Dependence of the Law” (Chapter 11 of this volume), Clayton Gillette brings Holmes's prediction home, perhaps to the surprise (but undoubtedly to the posthumous delight) of the master himself, turning to economics to study the vision of the law that Holmes put forth. From this we get a sharp picture of what a Holmes with access to modern neoclassical economic analysis of law could have said about optimal degrees of deference to precedent. My claim in this essay is that even if Holmes could have said these things that Gillette sets out in modern terms, he did not. Not because Gillette is in error about the economic approach to the question of optimality and precedent, but because Holmes was not engaged in an analytical project in The Path of the 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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".