Bibliographic record
Abstract
Citation (2015), "List of Contributors", Special Issue: Thinking and Rethinking Intellectual Property (Studies in Law, Politics, and Society, Vol. 67), Emerald Group Publishing Limited, Bingley, p. vii. https://doi.org/10.1108/S1059-433720150000067007 Publisher: Emerald Group Publishing Limited Copyright © 2015 Emerald Group Publishing Limited Abraham Drassinower University of Toronto Faculty of Law, Toronto, Canada Debora Halbert University of Hawaii at Manoa, Honolulu, HI, USA Laura A. Heymann William & Mary Law School, Williamsburg, VA, USA Liam Séamus O’Melinn Pettit College of Law, Ohio Northern University, Ada, OH, USA Peter K. Yu Intellectual Property Law Center, Drake, University Law School, Des Moines, IA, USA Book Chapters Special Issue: Thinking and Rethinking Intellectual Property Studies in Law, Politics, and Society Special Issue: Thinking and Rethinking Intellectual Property Copyright Page List of Contributors Editorial Board Tales of the Unintended in Copyright Law Dialogues of Authenticity Subject Matter, Scope, and User Rights in Copyright Law Property without Bounds and the Mythology of Common Law Copyright The Everyday Lives of Copyright
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.704 | 0.699 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".