Reviewer Acknowledgements for Journal of Education and Training Studies, Vol. 13, 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 13, Number 1 Alphonce John Amuli, ADEM, TanzaniaCarlos Sanz, Cardiff University, UKChosang Tendhar, Hackensack Meridian School of Medicine, USAEbenezer Kofi Enninful, Takoradi Technical University, GhanaEbrahim Mohammadpour, Farhangian University , IranHelena Reis, Polytechnic Institute of Leiria, PortugalJacinthe Rihan, Ain Shams University, EgyptJane Liang, California Department of Education, USAJoel Maligaya Cuadra, NU Laguna, PhilippinesJohn Mark Asio, Gordon College, PhilippinesJonathan Chitiyo, University of Pittsburgh Bradford, USAJulio Cesar André, Center for Studies and Development of Health Education-CEDES, BrazilKun Yu, Universiti Putra Malaysia, ChinaLinda J. Rappel, Yorkville University/University of Calgary, CanadaLisa Marie Portugal, American College of Education, USAMeral Seker, Alanya Alaaddin Keykubat University, TurkeyNiveen M. Zayed, MENA College of Management, JordanRichard Penny, University of Washington Bothell, USAShanita L. Pettaway, University of South Alabama, USA 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
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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 | 0.095 | 0.635 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.053 | 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".