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 14, Number 3 Agnese Lastovska, University of Latvia, Latvia Alaa Aladini , Dhofar University, Oman Alina Mag, University Lucian Blaga of Sibiu, Romania Anna Liduma, University of Latvia, Latvia Antonina Lukenchuk, National Louis University, USA Arbabisarjou Azizollah, Zahedan University of Medical Sciences, Iran Ariadna Llorens, Business Department, Spain Chia Jung Yeh, East Carolina University, USA Cristina Dumitru, The National University of Science and Technology, Romania Dede Salim Nahdi, Universitas Majalengka, Indonesia Ercan Tomakin, Ordu University, Turkey Ezgi Pelin Yildiz, Kafkas University in KARS, Turkey Fatma Elhassan, University of Hafr Al Batin, Saudi Arabia Firouzeh Sepehrianazar, Orumieh university, Iran Hadiyanto, Universitas Jambi, Indonesia Halupa Colleen, East Texas Baptist University Marshall, USA Hongyan Wang, Binghamton University, USA Isaiah M. Makhetha, National University of Lesotho, Lesotho Jacquelyn Benchik-Osborne, Chicago State University, USA Jayanti Dutta, Panjab University, India Laid Fekih, University of Tlemcen Algeria, Algeria Lalith Edirisinghe, CINEC Campus, Sri Lanka Lung-Tan Lu, Fo Guang University, Taiwan Miguel Flores, National College of Ireland, Ireland Nayereh Shahmohammadi, Academic Staff in Organization for Educational Research and Planning, Iran Pedro Tadeu, Centre for Studies in Education and Innovation CI&DEI-ESECD-IPG, Portugal Qing Xie, Jiangnan University, China Rafizah Mohd Rawian, Universiti Utara Malaysia, Malaysia Ranjit Kaur Gurdial Singh, The Kilmore International School, Australia Rebecca Cacho, De La Salle University, Philippines Sadeeqa Saleha, Lahore College for Women University Lahore, Pakistan Salwa Mohamed, Manchester Metropolitan University, UK Sarasa-Cabezuelo Antonio, Universidad Complutense de Madrid, Spain Sharmila Sivalingam, Maryville University of St.Louis, USA Tony Patrick George, Njala University, Sierra Leone Zahra Shahsavar, Shiraz University of Medical Sciences, Iran
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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 | 0.060 | 0.441 |
| 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 | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.096 | 0.062 |
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".