Case Global: Faculty and students making a global impact in extraordinary times
Bibliographic record
Abstract
Vol. 16, No. 1 (2024) CWRU International Law student leader selected as National Jurist Law Student of the Year CWRU Among the Best Three Decades of Global Impact Case Western Reserve Journal of International Law Tackles Global Climate Change The only Law School with a Foreign Policy Radio Program International Law Moot Court Powerhouse Celebrating the 75th Anniversary of the Universal Declaration and CWRU’s historic contributions to human rights CWRU law students develop Holocaust Memorial curriculum Yemen Accountability Project publishes fifth White Paper Financial Integrity Institute Expands Immigration Law program marks an extraordinary year International internships Capstone Placements take Students from the Hague to Freetown CWRU Law Alum Philip Hadji appointed Judge of the U.S. Court of Federal Claims Our international law faculty Alumni on the Global Stage 2023-24 International Law Conferences and Speakers Roundup Experts debate role of international law in response to the global climate change crisis CWRU School of Law bestows Humanitarian Award for Advancing Global Justice on Leila Sadat, the 2023 Klatsky Lecturer Law alum, Canadian Minister Francois-Philippe, presents a fireside chat School of Law Alumnus Richard Batsom, Judge Advocate General of the Coast Guard Student Leaders Honored
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".