Bibliographic record
Abstract
NOTICE TO CONTRIBUTORS1. The Journal invites the submission of unsolicited manuscripts. Submissions and correspondence concerning publications should be addressed to Editor-in-Chief, Journal of Law and Commerce, University of Pittsburgh School of Law, Barco Law Building, 3900 Forbes Avenue, Pittsburgh, PA 15260.2. The Journal requests that manuscripts be accompanied by an abstract of not more than 200 words describing the contents of the article.3. Footnotes should conform to The Bluebook: A Uniform System of Citation (20th ed. 2015).4. All manuscripts, including footnotes and abstracts, should be typed and submitted directly to the website.Published twice yearly: Fall, SpringAnnual Subscription Rate: U.S. ‑ $20.00; Foreign ‑ $25.00Internet Address: http://jlc.law.pitt.edu/E-mail Address: jlc.law@mail.pitt.eduSingle copies of Volume 36 are $11.00 and may be ordered from the Business Manager, Journal of Law and Commerce, University of Pittsburgh School of Law, Barco Law Building, 3900 Forbes Avenue, Pittsburgh, PA 15260.Volumes 1 through 35 may be ordered from William S. Hein & Co., Inc., 1285 Main Street, Buffalo, NY 14209; (800) 828-7571.If subscription is to be discontinued at expiration, notice to that effect should be sent to the Journal office, otherwise it will be renewed.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.934 | 0.923 |
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".