PCAC 2007 Organizing and Program Committees
Bibliographic record
Abstract
Paolo Bellavista, University of Bologna, Italy Pascal Chatonnay, NUMERICA/ISTI/LIFC, France Yuh-Shyan Chen, National Taipei University, Taiwan Sunghyun Choi, Seoul National University, South Korea Chun Tung Chou, University of New South Wales, Australia Tarik Cicic, Simula Research Laboratory, Norway Felipe A. Cruz-Perez, CINVESTAV-IPN, Mexico Fei Dai, North Dakota State University, USA Falko Dressler, University of Erlangen, Germany Paal E. Engelstad, Telenor R&D, Norway Carles Gomez, Technical University of Catalonia, Spain Peter C.J. Graham, University of Manitoba, Canada Jadwiga Indulska, The University of Queensland, Australia Susumu Ishihara, Shizuoka University, Japan Andreas Kassler, Karlstad University, Sweden Dimitrios Katsaros, Aristotle University, Greece Guanling Lee, National Dong Hwa University, Taiwan Jie Li, University of Tsukuba, Japan Leszek T. Lilien, Western Michigan University, USA Jiangchuan Liu, Simon Fraser University, Canada Luigi Logrippo, Universite du Quebec en Outaouais, Canada Seng Wai Loke, Latrobe University, Australia Cecilia Mascolo, University College London, UK Chris McDonald, The University of Western Australia, Australia Yongwan Park, Yeung Nam University, South Korea Elhadi Shakshuki, Acadia University, Canada Timothy K. Shih, Tamkang University, Taiwan Tor Skeie, Simula Research Laboratory, Norway Limin Sun, Institute of Software, China Academy of Science, China Xueyan Tang. Nanyang Technological University, Singapore Torab Torabi, Latrobe University, Australia Javier Garcia Villalba, Complutense University of Madrid, Spain Jianping Wang, University of Mississippi, USA
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.251 | 0.316 |
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".