Stefan A. Riesenfeld Award Lecture - Crimes against Women under International Law
Bibliographic record
Abstract
Thank you, Dean Dwyer.Let me add a welcome, on behalf of the International Legal Studies Program, to all of you, and especially to our guest speaker.It is great to see so many people here, especially in light of the significance of the topic that will be discussed.I also want to add my congratulations to the Berkeley Journal of International Law for yet again carrying off an event seemingly without a flaw.My role here is to introduce our speaker, which is an honor and a pleasure.There are really two ways in which it can be difficult to introduce someone.One is if their accomplishments are so few in number and so small in magnitude that it is difficult to know really what to say.The other one is if their accomplishments are so large in number and so impressive in magnitude that, without occupying the entire time left to the speech, you have to decide what you are going to say.I face the second of these problems.I am going to just do my best to deal with it, recognizing that I'm omitting a whole bunch of important information, and hoping that Justice Arbour will not take offense.She is referred to as Justice Arbour because she is a member of the Canadian Supreme Court, and has been since 1999.Before that, she was a judge on the Court of Appeals for Ontario and, before that, was on the Supreme Court of Ontario.I do not need to tell anybody here that having Supreme Court Justice on your resume is a pretty good thing, and not a bad justification for us to have 1.This piece was transcribed from the 2002 Stefan A. Riesenfeld Award Lecture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.032 |
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".