The Oxford Handbook of Law and Economics: Volume 3: Public Law and Legal Institutions
Bibliographic record
Abstract
Covering over one-hundred topics on issues ranging from Law and Neuroeconomics to European Union Law and Economics to Feminist Theory and Law and Economics, The Oxford Handbook of Law and Economics is the definitive work in the field of law and economics. The book gathers together scholars and experts in law and economics to create the most inclusive and current work on law and economics. Edited by Francisco Parisi, the Handbook looks at the origins of the field of law and economics, tracks its progression and increased importance to both law and economics, and looks to the future of the field and its continued development by examining a cornucopia of fields touched by work in law and economics. The uniqueness of its breadth, depth, and convenience make the volume essential to scholars, students, and contributors in the field of law and economics. Contributors to this volume - Robert G. Bone, The University of Texas at Austin School of Law Roger Bowles, University of York Anthony Braga, Rutgers University Mariano-Florentino Cuellar, Stanford University Andrew Daughety, Vanderbilt University Ben Depoorter, University of California, Hastings Isaac Ehrlich, University at Buffalo Winard Emons, University of Bern Richard A. Epstein, The New York University School of Law John M. de Figueiredo, Duke University Talia Fisher, Tel Aviv University Thomas Ginsburg, University of Chicago Law School Mitu Gulati, Duke University Andrew Guzman, USC Gould School of Law Keith Hylton, Boston University School of Law Robert Inman, Wharton School, University of Pennsylvania Daniel Klerman, USC Law School Bruce Kobayashi, George Mason Law School Jerry Mashaw, Yale University Geoffrey Miller, New York University Law School Erin O'Hara, Vanderbilt Law School Jennifer Reinganum, Vanderbilt University Paul Rubin, Emory University Daniel Rubinfeld, University of California, Berkeley and NYU Chris W. Sanchirico, University of Pennsylvania Daniel Shaviro, New York University Law School Paul B. Stephan, University of Virginia School of Law Edward H. Stiglitz, Cornell University Alan Sykes, Stanford Law School Joel Trachtman, Tufts University W. Mark C. Weidemaier, North Carolina at Chapel Hill Albert Yoon, University of Toronto Faculty of 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.019 |
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".