Bibliographic record
Abstract
In October 2002 I was lucky enough to spend three stimulating days at the New York University Law School Library participating in the annual Legal Information Transfer Network workshop. The Legal Information Transfer Network (ITN) is funded by a generous grant from The Starr Foundation (established in 1955 by insurance entrepreneur Cornelius Van der Starr) and is headed by the dynamic Director of the NYU Law School Library, Professor Kathie Price. ITN aims to establish a global network of prestigious law libraries which ultimately can offer a 24/7 virtual reference service, both to its own partner libraries in the developed world and to academic legal communities in less developed countries. Previous annual workshops in such cities as Lausanne in Switzerland have given senior librarians from ITN partner libraries the opportunity to meet and make progress on issues such as providing a global virtual reference desk, sharing database access across the libraries, developing interactive legal research guides, and creating imaginative training programmes for local law librarians in China and Southern Africa (http://www.law.nyu.edu/library/itn). Between workshops the exchange of ideas is continued by email discussion. Currently the list of law library partners includes New York University, Washington University in Seattle, Toronto University in Canada, IALS Library in the UK, the Catholic University of Leuven in Belgium, Tilburg University in the Netherlands, Konstanz University in Germany, Cape Town University in South Africa, Melbourne University in Australia, Yerevan State University in Armenia, and Tsinghua University in China.
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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 | 0.063 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.051 | 0.031 |
| Scholarly communication | 0.092 | 0.081 |
| Open science | 0.007 | 0.066 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.073 | 0.029 |
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".