Bibliographic record
Abstract
RULES OF EVIDENCE (Thomson-West, 3d ed.2006), and numerous articles.He has been special counsel or consultant on matters of evidence, including the Federal Rules of Evidence, and related topics, to both Houses of Congress, the National Conference of Commissioners on Uniform State Laws, the National Academy of Sciences, the Federal Judicial Center, Rand, AEI-Brookings, and the Government of Canada, among others.He chaired the Association of American Law Schools Evidence Section and an American Bar Association committee monitoring developments under the Federal and Uniform Rules of Evidence that suggested changes to the Rules, a number of which have been made.His series of national conferences on the Federal Rules of Evidence just before they came out, and his accompanying book, the first on the Federal Rules of Evidence, are credited with introducing the bench, bar, and much of academia to what they would be facing under the new Rules.He has also been consultant on legal, judicial, and constitutional reform for over a dozen countries, mostly those emerging from the former Soviet Union, such as Russia, Ukraine, the Slovak Republic, Hungary,
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 | 0.024 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.186 | 0.070 |
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".