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 8, Number 3Ana-Cornelia Badea, Technical University of Civil Engineering Bucharest, RomaniaAntonina Lukenchuk, National Louis University, USAArbabisarjou Azizollah, Zahedan University of Medical Sciences, IranAusra Kazlauskiene, Siauliai University, LithuaniaÇelebi Uluyol, Gazi University, Turkey, TurkeyDonna Harp Ziegenfuss, The University of Utah, USADonna.Smith, The Open University, UKFirouzeh Sepehrian Azar, Orumieh University, IranGerard Hoyne, School of Health Sciences, University of Notre Dame Australia, AustraliaGregory S. Ching, Fu Jen Catholic University, TaiwanHermes Loschi, University of Campinas, Braziljames badger, University of North Georgia, USAJisun Jung, University of Hong Kong, Hong KongJohn Cowan, Edinburgh Napier University, United KingdomJohn Lenon Ednave Agatep, AMA Computer College, PhilippinesLaid Fekih, University of Tlemcen Algeria, AlgeriaMichael John Maxel Okoche, Uganda Management Institute, UgandaNajia Sabir, Indiana University Bloomington, USANicos Souleles, Cyprus University of Technology, CyprusQing Xie, Jiangnan University, ChinaRanjit Kaur Gurdial Singh, The Kilmore International School, AustraliaSakiru Abiodun, Adeniran Ogunsanya College of Education, NigeriaSandhya Rao Mehta, Sultan Qaboos University, IndiaSavitri Bevinakoppa, Melbourne Institute of Technology, AustraliaTeguh Budiharso, Center of Language and Culture Studies, IndonesiaVasiliki Brinia, Athens University of Economic and Business, GreeceYi Luo, University of Illinois at Urbana- Champaign, USA
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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 | 0.080 | 0.546 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.089 | 0.049 |
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".