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 3 Arbabisarjou Azizollah, Zahedan University of Medical Sciences, Iran Arwa Aleryani, Saba University, Yemen Aurora-Adina Colomeischi, Stefan cel Mare University, Romania Aynur Yürekli, İzmir University of Economics, Turkey Bo Chang, Ball State University, USA Carmen P. Mombourquette, University of Lethbridge, Canada Evrim Ustunluoglu, Izmir University of Economics, Turkey Ezgi Pelin Yildiz, Kafkas University in KARS, Turkey Huda Fadhil Halawachy, University of Mosul, Iraq Hüseyin Serçe, Selçuk University, Turkey Jayanti Dutta, Panjab University, India John Rafferty, Charles Sturt University, Australia John W. Miller, Benedict College, USA Kartheek R. Balapala, University Tunku Abdul Rahman, Malaysia Mei Jiun Wu, Faculty of Education, University of Macau, China Meric Ozgeldi, Mersin University, Turkey Minna Körkkö, Unversity of Lapland, Finland Mirosław Kowalski, University of Zielona Góra, Poland Muhammad Ishtiaq Ishaq, Global Institute Lahore, Pakistan Nayereh Shahmohammadi, Academic Staff, Iran Oktavian Mantiri, Asia-Pacific International University, Thailand Qing Xie, Jiangnan University, China Rouhollah Khodabandelou, Sultan Qaboos University, Oman Saheed Ahmad Rufai, Lagos State University, Nigeria Salwa El-Sobkey, Modern University for Technology and Information, Egypt Savitri Bevinakoppa, Melbourne Institute of Technology, Australia Waldiney Mello, Universidade do Estado do Rio de Janeiro, Brazil Yvonne Joyce Moogan, Leeds University Business School, United Kingdom Zahra Shahsavar, Shiraz University of Medical Sciences, Iran
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 | 0.056 | 0.423 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.120 | 0.082 |
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".