Bibliographic record
Abstract
Table of Contents: Dean's Message Masthead Leading the Way in Law, Technology, and the Arts Technology and Law: From the Classroom to the Courtroom and Beyond The Honorable Kathleen M. O'Mallley '82 as Distinguished Visiting Jurist... Lecture Series Spring 2002 In Review 2002-2003 Lecture Series Career Services Employment Statistics... Class of 2001 Employers Lewis R. Katz, The LL.M. in United States Legal Studies Program -- A Year in Review Reunion 2002 Law Alumni Reunion & Educational Seminar in London, England Save the Date Law Alumni Reunion 2003 The School of Law Annual Fund Alumni and Faculty Luncheon in Cleveland November 22, 2002 ... Regional Alumni Events Student News Canada-U.S. Law Institute Holds Its Sixteenth Annual Conference at the School of Law 2002 Commencement Order of the Coif Graduation Honors and Awards Faculty Briefs 2002-2003 New Faculty (Nance and Scharf) Secretary of Defense Donald Rumsfeld Named Emeritus Professor Ollie Schroeder his "Favorite Teacher" We Thank Professors James McElhaney, Sidney Picker and Bryan Adamson for Their Years of Service to Our School Class Notes In Memoriam Rodney B. Pulliam, Class of 1999 1963-2002 Jeff Rice, '75 Umpires Super Bowl XXXVI Legacy Society Law Alumni Association School of Law Visiting Committee Calendar of Events
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.869 | 0.815 |
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".