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 find the application form and details at http://recruitment.ccsenet.org and e-mail the completed application form to hes@ccsenet.org. Reviewers for Volume 9, Number 2 Abdelaziz Mohammed, Albaha University, Saudi Arabia 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 Ausra Kazlauskiene, Siauliai University, Lithuania Aynur Yürekli, İzmir University of Economics, Turkey Bahar Gün, İzmir University of Economics, Turkey Bo Chang, Ball State University, USA Evrim Ustunluoglu, Izmir University of Economics, Turkey Gamze Kasalak, Akdeniz University, Turkey Gregory S. Ching, Fu Jen Catholic University, Taiwan Jisun Jung, University of Hong Kong, Hong Kong Karsten Krauskopf, University of Potsdam, Germany Lung-Tan Lu, Fo Guang University, Taiwan Meric Ozgeldi, Mersin University, Turkey Mirosław Kowalski, University of Zielona Góra, Poland Oana-Mihaela Rusu, Unviersity of Iasi, Romania Olusola Ademola Olaniyi, Prince Mohammad Bin Fahd University, Saudi Arabia Prashneel Ravisan Goundar, Fiji National University, Fiji Rafizah Mohd Rawian, Universiti Utara Malaysia, Malaysia Ranjit Kaur Gurdial Singh, The Kilmore International School, Australia Sumita Chowhan, Jain University, India Tuija A. Turunen, University of Lapland, Finland Uher Ivan, University P.J.Safarika Kosice, Slovakia 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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; both teacher heads agree on what is shown here.
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".