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 1 Anna Liduma, University of Latvia, Latvia Arbabisarjou Azizollah, Zahedan University of Medical Sciences, Iran Ercan Tomakin, Ordu University, Turkey Ezgi Pelin Yildiz, Kafkas University in KARS, Turkey Filomena Soares, Porto Accounting and Business School - Polytechnic of Porto, Portugal Florentine Paudel, University College of Teacher Education Vienna, Austria Halupa Colleen, East Texas Baptist University Marshall, USA Huda Fadhil Halawachy, University of Mosul, Iraq Isaiah M. Makhetha, National University of Lesotho, Lesotho Lung-Tan Lu, Fo Guang University, Taiwan Marlon Tayag, Holy Angel University, Philippines Mei Jiun Wu, Faculty of Education, University of Macau, China Pooya Taheri, BCIT, School of Energy, Canada Rebecca Cacho, De La Salle University, Philippines Rodulfo Aunzo, Visayas State University, Philippines Sadeeqa Saleha, Lahore College for Women University Lahore, Pakistan Sarasa-Cabezuelo Antonio, Universidad Complutense de Madrid, Spain Tony Patrick George, Njala University, Sierra Leone
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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 | 0.069 | 0.511 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.099 | 0.060 |
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".