International Advocate for Peace Award Acceptance Speech
Bibliographic record
Abstract
I feel very much at home here because I was here three or four year ago at a symposium at the law school-a panel with Adam Durshowitz and Erwin Cogler, who was then Administer of Justice of Canada, and others. I have also received an honorary doctorate from Yeshiva University, and I feel very much at home here. I just met Dean Dillard and Professor Love, but I have some longtime friends here: Professor Weisberg, who I will talk about in a moment, is one of the heroes of this whole episode; Eric Pan, your professor of Commercial and International Law, was one of our great associates at Covington Burling who had the good judgment not to fill out any more time sheets and to actually teach. And so it is really a particular pleasure to be here. And Jordan [Walerstein], thank you very much for everything you have done; I am very honored to receive the award, especially from the entity that is giving it to me in light of the past recipients.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.030 | 0.032 |
| Insufficient payload (model declined to judge) | 0.105 | 0.038 |
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".