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 15, Number 4 Abdelaziz Mohammed, Albaha University, Saudi Arabia Adiv Gal, Kibbutzim College of Education Technology and the Arts, Israel Alaa Aladini, Dhofar University, Oman Antonina Lukenchuk, National Louis University, USA Arbabisarjou Azizollah, Zahedan University of Medical Sciences, Iran Chia Jung Yeh, East Carolina University, USA Cristina Dumitru, The National University of Science and Technology, Romania Dede Salim Nahdi, Universitas Majalengka, Indonesia Dibakar Sarangi, Teacher Education and State Council for Educational research and Training, India Eman Elbealy, King Khalid University, Saudi Arabia Emma Groenewald, Sol Plaatje University, South Africa Ercan Tomakin, Ordu University, Turkey Ezgi Pelin Yildiz, Kafkas University in KARS, Turkey Fatma Elhassan, University of Hafr Al Batin, Saudi Arabia Fernando Acevedo, Universidad de la Repúblic, Uruguay Filomena Soares, Porto Accounting and Business School - Polytechnic of Porto, Portugal Firouzeh Sepehrianazar, Orumieh university, Iran Florentine Paudel, University College of Teacher Education Vienna, Austria Gabin Fernandes Balou, Marien NGOUABI University, Congo Hadiyanto, Universitas Jambi, Indonesia Hongyan Wang, Binghamton University, USA Huda Fadhil Halawachy, University of Mosul, Iraq Hung Thu Phan, Vinh University, Viet Nam Isaiah M. Makhetha, National University of Lesotho, Lesotho Jacquelyn Benchik-Osborne, Chicago State University, USA Jayanti Dutta, Panjab University, India Joey Mata Villanueva, Nueva Vizcaya State University, Philippines John Cowan, Edinburgh Napier University, United Kingdom John W. Miller, University of Louisville, USA Kartheek R. Balapala, University Tunku Abdul Rahman, Malaysia Kholood Moustafa Alakawi, Al-Imam Muhammad Ibn Saud Islamic University, Saudi Arabia Lalith Edirisinghe, CINEC Campus, Sri Lanka Lewis Entwistle, University of Manchester, UK Lung-Tan Lu, Fo Guang University, Taiwan Marlon Tayag, Holy Angel University, Philippines Mei Jiun Wu, Faculty of Education, University of Macau, China Michael Eneye Abdullahi, Boston College, USA Miguel Flores, National College of Ireland, Ireland Mirosław Kowalski, University of Zielona Góra, Poland Moosa Fateel, University of Bahrain, Bahrain Mpoki Mwaikokesya, University of Dar-es-Salaam, Tanzania Muhammad Aswad, Universitas Sulawesi Barat, Indonesia Noel Jimbai Balang, Selangau Education Office, Malaysia Oktavian Mantiri, Asia-Pacific International University, Thailand Oluwatosin Lagoke, Oxford Brookes University, United Kingdom Osman Cekic, Canakkale Onsekiz Mart University, Turkey Prashneel Ravisan Goundar, University of New England, Australia Qing Xie, Jiangnan University, China Rafizah Mohd Rawian, Universiti Utara Malaysia, Malaysia Sadeeqa Saleha, Lahore College For Women University Lahore, Pakistan Samuel Byndom, Parkland College, United States, United States Sarah Wolff, University of Nevada Las Vegas, USA Sarasa-Cabezuelo Antonio, Universidad Complutense de Madrid, Spain Saravanan Sathiyaseelan, Nil, Singapore Sharmila Sivalingam, Maryville University of St.Louis, USA Sumita Chowhan, Jain University, India Susan Gasson, James Cook University, Australia Tony Patrick George, Njala University, Sierra Leone Vilma Geronimo, Laguna State Polytechnic University, Philippines 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 | 0.070 | 0.496 |
| 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 | 0.015 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.097 | 0.063 |
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".