Bibliographic record
Abstract
Higher Education Studies wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal are greatly appreciated. Higher Education Studies is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please contact us for the application form at: hes@ccsenet.org Reviewers for Volume 10, Number 1 Antonina Lukenchuk, National Louis University, USA Aynur Yürekli, İzmir University of Economics, Turkey Bahar Gün, İzmir University of Economics, Turkey Barbara N. Martin, University of Central Missouri, USA Cristina Sin, CIPES (Centre for Research in Higher Education Policies), Portugal Deniz Ayse Yazicioglu, Istanbul Technical University, Turkey Donna.Smith , The Open University, UK Hüseyin Serçe, Selçuk University, Turkey James Badger, University of North Georgia, USA Laith Ahmed Najam, Mosul University, IRAQ Meric Ozgeldi, Mersin University, Turkey Mpoki Mwaikokesya, University of Dar-es-Salaam, Tanzania Nicos Souleles, Cyprus University of Technology, Cyprus Olusola Ademola Olaniyi, Prince Mohammad Bin Fahd University, Saudi Arabia Prashneel Ravisan Goundar, Fiji National University, Fiji Robin Rawlings, Walden University, USA Sadeeqa Sadeeqa, Lahore College for Women University Lahore, Pakistan Savitri Bevinakoppa, Melbourne Institute of Technology, Australia Semiyu Adejare Aderibigbe, University of Sharjah, UAE Teguh Budiharso, Center of Language and Culture Studies, Indonesia Yousef Ogla Almarshad, Aljouf University, Saudi Arabia
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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 | 0.072 | 0.506 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.123 | 0.080 |
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".