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 5, Number 12Anne M. Hornak, Central Michigan University, USAAntónio Calha, Polytechnic Institute of Portalegre, PortugalAubri Rote, University of North Carolina at Asheville, USACagla Atmaca, Pamukkale University, TurkeyErica D. Shifflet-Chila, Michigan State University, USAFatma Ozudogru, Usak University, TurkeyIntakhab Khan, King Abdulaziz University, Saudi ArabiaIoannis Syrmpas, University of Thessaly, GreeceJohn Bosco Azigwe, Bolgatanga Polytechnic, GhanaJohn Cowan, Edinburgh Napier University, UKKatya De Giovanni, University of Malta, MaltaLaima Kyburiene, Kaunas University of Applied Sciences, LithuaniaLinda J. Rappel, Yorkville University/University of Calgary, CanadaLisa Marie Portugal, Grand Canyon University, USALorna T. Enerva, Polytechnic University of the Philippines, PhilippinesMarcie Zaharee, The MITRE Corporation, USAMarco Antonio Catussi Paschoalotto, University of São Paulo, BrazilMaria Pavlis Korres, Hellenic Open University, GreeceMatthews Tiwaone Mkandawire, Central China Normal University, MalawiMaurizio Sajeva, Pellervo Economic Research PTT, FinlandMehmet Inan, Marmara University, TurkeyMeral Seker, Alanya Alaaddin Keykubat University, TurkeyMichail Kalogiannakis, University of Crete, GreeceMin Gui, Wuhan University, ChinaMu-hsuan Chou, Wenzao Ursuline University of Languages, TaiwanMustafa Uğraş, Fırat University, TurkeyNele Kampa, Leibniz-Institute for Science and Mathematics Education (IPN), GermanyPhil Sirinides, University of Pennsylvania, USAPuneet S. Gill, Texas A&M International University, USARichard H. Martin, Mercer University, USASamad Mirza Suzani, Islamic Azad University, IranSelloane Pitikoe, University of Kwazulu-Natal, South AfricaSimona Savelli, Università degli Studi Guglielmo Marconi, ItalySisi Chen, American University of Health Sciences, USAStamatis Papadakis, University of Crete, GreeceSuzan Kavanoz, Yıldız Technical University, TurkeyThomas K. F. Chiu, The University of Hong Kong, Hong KongTilanka Chandrasekera, Oklahoma State University, USAVeronica Rosa, University Rome, ItalyYerlan Seisenbekov, Kazakh National Pedagogical University, Kazakhstan Robert SmithEditorial AssistantOn behalf of,The Editorial Board of Journal of Education and Training StudiesRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: 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 | 0.056 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.119 | 0.067 |
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".