National treaty law and practice : dedicated to the memory of Monroe Leigh
Bibliographic record
Abstract
Dedication, Duncan B. Hollis, Merritt R. Blakeslee, & L. Benjamin Ederington, About the Contributors, Chapter 1: A Comparative Approach to Treaty Law and Practice, Duncan B. Hollis, Chapter 2. Austria, Franz Cede and Gerhard Hafner, Chapter 3. Canada, Maurice Copithorne, Chapter 4. Chile, Francisco Orrego Vicuna and Francisco Orrego Bauza, Chapter 5. China, Ambassador Xue Hanqin, Hu Zhiqiang, and Fan Kun, Chapter 6. Colombia, German Cavelier, Chapter 7. Egypt, Judge Nabil Elaraby, Mohammed Gomaa, and Lamia Mekhemar, Chapter 8. France, Pierre Michel Eisemann and Raphaele Rivier, Chapter 9. Germany, Hubert Beemelmans and Hans D. Treviranus, Chapter 10. India, K. Thakore, Chapter 11. Israel, Ruth Lapidoth, Chapter 12. Japan, Ambassador Takao Kawakami, Chapter 13. Mexico, Luis Miguel Diaz, Chapter 14. The Netherlands, Jan G. Brouwer, Chapter 15. Russia, William E. Butler, Chapter 16. South Africa, Neville John Botha, Chapter 17. Switzerland, Luzius Wildhaber, Marc D. Schinzel, and Adrian Scheidegger, Chapter 18. Thailand, Sompong Sucharitkul, Chapter 19. United Kingdom, Sir Ian Sinclair, Susan J. Dickson, and Graham Maciver, Chapter 20. United States, Robert E. Dalton, Acknowledgements, Index
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".