Bibliographic record
Abstract
Table of Contents: Dean's Message Masthead The Library of the Future -- The Judge Ben C. Green Library Reflections on Judge Ben C. Green '30 Leading the Way in Legal Education Campaign Update Society of Benchers Inducts Seven New Members Center for Law, Technology, and the Arts Spring 2004 Calendar of Events... Admissions Career Services Class of 2002 Employers Hiram E. Chodosh, Curricular Innovations The LL.M. in United States and Global Legal Studies Program -- An Update Diego Archer '03 Interns at International Criminal Tribunal Canada-U.S. Law Institute Holds its Seventeenth Annual Conference at School of Law Reunion 2003 The Law-Medicine Center 50th Anniversary Celebration Regional Alumni Events The 2003-2004 Annual Fund Lecture Series Fall 2003 In Review Spring 2004 Lecture Series Student News Student Organization Spotlight and News 2003 Commencement Order of the Coif Graduation Honors and Awards Excerpts from Keynote Address by Michael Cherkasky, '75 2003-2004 New Faculty (Arewa, Boise, Dillman, Ku) Faculty Briefs Class Notes In Memoriam Help Us Reconnect... Law Alumni Association Special Opportunity 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 | 0.005 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.780 |
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".