Bibliographic record
Abstract
Lars Bjornshauge, Chairman of the Task Force reports that it was established following the endorsement of IFLA's Statement on Open Access and the subsequent approval of a number of key initiatives The taskforce will work on the following issues: Advocate for the adoption and promotion of open access policies as set out in IFLA's Statement on Open Access within the framework of the United Nations institutions (UN, UNESCO, WHO, FAO) Build Capacity within the IFLA Membership to advocate for the adoption of open access policies at the national level, through the development of case studies and best practices for open access promotion Furthermore the taskforce will connect to the various organizations working for Open Access – as indicated in the statement -such as SPARC (US/Europe/Japan), COAR, OASPA, EIFL, Bioline International & DOAJ, among others. The taskforce has the following members: Lars Bjornshauge (CHAIR), 1st Vice-President, Swedish Library Association Leslie Chan, Associate Director, Bioline International, University of Toronto at Scarborough Jan Hagerlid, Programme Co-ordinator of OpenAccess.se, National Library of Sweden Iryna Kuchma, EIFL.Net Open Access Manager, EIFL, Rome, Italy Rick Luce, Vice Provost and Director of Libraries, Emory University, USA Felipe Martinez, Director, University Center for Library Science Research, National Autonomous University of Mexico Bas Savenijie, Director, National Library of the Netherlands Xuemao Wang, Associate Vice-Provost, Emory University Libraries, Emory University, USA Qiang Zhu, Director, Peking University Library, Beijing, China Ann Okerson, Special Advisor on Electronic Strategies, Center for Research Libraries New Haven, CT, United States Derek Law, Professor, University of Strathclyde, Glasgow, United Kingdom
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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 | 0.136 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.086 | 0.081 |
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".