Bibliographic record
Abstract
Roundtable Moderator:\nAmos Guiora, Professor of Law, Co-Director of the Center for Global Justice, University of Utah S.J. Quinney College of Law.\nRoundtable Participants:\nHarry Soyster, United States Army Lieutenant General (Ret.); former Director, Defense Intelligence Agency;\nDavid Irvine, United States Army Brigadier General (Ret.); former Deputy Commander for the 96th Regional Rediness Command;\nGeoffrey S. Corn, Professor of Law; Presidnetial Research Professor, South Texas College of Law;\nJames Carafano, Vice President, Foreign and Defense Policy Studies; E.W. Richardson Fellow; and Director of the Kathryn and Shelby Cullom Davis Institute for International Studies, The Heritage Foundation;\nClaire Finkelstein, Algernon Biddle Professor of Law and Professor of Philosophy, Director of the Center for Ethics and the Rule of Law, University of Pennsylvania;\nLaurie Blank, Director of the Internation Humanitarian Law Clinic, Emory University School of Law;\nMonica Hakimi, Professor of Law, Associate Dean for Academic Programming, University of Michigan Law School;\nGeorge R. Lucas, Professor of Ethics and Public Policy, Naval Postgraduate School;\nTrevor Morrison, Libiu Librescu Professor Law, Columbia Law School; and\nFrédéric Mégret, Associate Professor of Law, Associate Dean of Research (Law); Canada Research Chair in the Law of Human Rights and Legal Pluralism, McGill University.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.706 | 0.439 |
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".