Reviewer Acknowledgements for Journal of Education and Training Studies, Vol. 14, No. 1
Bibliographic record
Abstract
Journal of Education and Training Studies (JETS) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether JETS publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 14, Number 1 Agnes Dulu Qhibi, AMESA, South AfricaAhmadreza Mohebbi, University of Auckland, New ZealandArdalan Kaveh, Islamic Azad University, IranAshraf Elsafty, Eslsca University, EgyptChosang Tendhar, Hackensack Meridian School of Medicine, USAEbenezer Kofi Enninful, Takoradi Technical University, GhanaEbrahim Mohammadpour, Farhangian University , IranEhab Gouda Tolba, Mansoura University, EgyptEman Elbealy, Kiing Khalid University, Saudi ArabiaFathia Lahwal, Elmergib University, LibyaFatima Zahra El Ouahabi, CRMEF, MoroccoHelena Reis, Polytechnic Institute of Leiria, PortugalHossein Chaharbashloo, Kharazmi University, IranHugh Jiliang Liu, University of Groningen, The NetherlandsIntakhab Khan, King Abdulaziz University, Saudi ArabiaJacinthe Rihan, Ain Shams University, EgyptJane Liang, California Department of Education, USAJoel Maligaya Cuadra, NU Laguna, PhilippinesJohn Cowan, Edinburgh Napier University, UKJohn Mark Asio, Gordon College, PhilippinesJongho Park, University of Michigan, USAJorge Pozuelo-Muñoz, University of Zaragoza, SpainJulio Cesar André, CEDES, BrazilKaren Wallace, University of Newcastle, AustraliaKendall Hartley, University of Nevada, USAKun Yu, Universiti Putra Malaysia, ChinaLeila Youssef, Arab Open University, LebanonLinda J. Rappel, Yorkville University/University of Calgary, CanadaLisa Marie Portugal, American College of Education, USAMaria Pavlis Korres, Hellenic Open University, GreeceMartin Kölbel, University College of Teacher Training Vienna, AustriaMaurizio Sajeva, Pellervo Economic Research PTT, FinlandMustafa Çakır, Marmara University, TurkeyNonhlanhla Cleopatra Masinga, University of South Africa, South AfricaÖzlem Ulaş-Kılıç, Giresun University, TurkeyRichard Penny, University of Washington Bothell, USASabrine Chbichib, The Higher Institute of Computing&Multimedia Gabes Tunisia, TunisiaSamson Chama, Alabama A & M University, USASarah Nabih Nasif, October University, EgyptShanita L. Pettaway, University of South Alabama, USAShu-wen Lin, Fu Jen Catholic University, TaiwanStamatis Papadakis, University of Crete, GreeceZinaide Gruda, University of Prishtina, Kosovo Robert SmithEditorial AssistantOn behalf of, The Editorial Board of Journal of Education and Training StudiesRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAE-mail: jets@redfame.comURL: http://jets.redfame.com
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 | 0.083 | 0.607 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.054 | 0.028 |
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".