Bibliographic record
Abstract
Program Committee Rachid Anane, University of Coventry, UK Irfan Awan, University of Bradford, UK Kuo-Ming Chao, University of Coventry, UK Arjan Durresi, Indiana University Perdue University Indianapolis, USA Katherine Guo, Bell Labs, USA Takahiro Hara, Osaka University, Japan Hui-Huang Hsu, Tamkang University, Taiwan Runhe Huang, Hosei University, Japan Qun Jin, Waseda University, Japan Hiroaki Kikuchi, Tokai University, Japan Akio Koyama, Yamagata University, Japan Jiandong Li, Xidian University, China Kuan-Ching Li, Providence University, Taiwan Janhua Ma, Hosei University, Japan Takuo Nakashima, Tokai University, Japan Hiroaki Nishino, Oita University, Japan Masato Oguchi, Ochanomizu University, Japan Wenny Rahayu, La Trobe University, Australia Fumiaki Sato, Toho University, Japan Elhadi Shakshuki, Acadia University, Canada Timothy K. Shih, Tamkang University, Taiwan David Taniar, Monash University, Australia Cristian Tchepnda, France Telecom R&D, France Minoru Uehara, Toyo University, Japan Quincy Wu, National Chi Nan University, Taiwan Fatos Xhafa, Polytechnic University of Catalonia, Spain Muhammad Younas, Oxford Brookes University, UK Vamsi Paruchuri, University of Central Arkansas, USA Mieso Denko, University of Guelph, Canada
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.211 | 0.234 |
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".