LAW AND ECONOMICS IN THE LEGAL ACADEMY, OR, WHAT I SHOULD HAVE SAID TO DISCIPULUS
Bibliographic record
Abstract
Is there a future for law and economics scholarship within the legal academy that does not involve formal modelling? To reach a positive answer to this question, I provide a brief sketch of the development of the subdiscipline, showing how, in the most recent period, the dominance of economists, working with their own agenda and career motivations, has created obstacles for the dissemination of law and economics within the legal academy and to legal policy makers more generally. I argue that drawing out and communicating to this broader readership the major insights of law and economics remain important tasks. So also, from a normative perspective, to relate efficiency analysis to whatever non-economic goals may also influence particular areas of law. Across the huge range of his publications, Michael Trebilcock has provided a model for the law and economics scholarship that I am advocating.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".