Bibliographic record
Abstract
The economic analysis of law has gone through a remarkable change in the past decade and a half. The founding articles of the discipline – such classic pieces as Ronald Coase's “The problem of social cost” (1960), Richard Posner's “A theory of negligence” (1972) and Guido Calabresi and Douglas Malamed's “Property rules, liability rules, and inalienability: One view of the cathedral” (1972) – offered economic analyses of familiar aspects of the common law, seeking to explain, in particular, fundamental features of the law of tort in terms of such economic ideas as transaction costs (Coase), Kaldor-Hicks efficiency (Posner), or minimizing the sum of the accident costs and avoidance costs (Calabresi and Malamed). In each case, they argued that the law of torts should be understood as a set of liability rules selected for their incentive effects, rather than as a set of substantive rights and remedies for their violation. These authors claimed to be able to explain most of the features of tort law and, where features were found that did not fit with their preferred explanations, recommended modification. Although they disagreed on important questions, each of the pieces seems to work a manageable structure into what strikes first-year law students as an otherwise random morass of common-law judgments. Generations of legal academics were introduced to these works, and drawn into their way of looking at things. As a student studying first-year torts with Calabresi at Yale, I had the sense that I was in the presence of greatness.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.014 | 0.034 |
| Insufficient payload (model declined to judge) | 0.049 | 0.030 |
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".