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 7, Number 3Enisa Mede, Bahcesehir University, TurkeyFatma Ozudogru, Usak University, TurkeyFroilan D. Mobo, Philippine Merchant Marine Academy, PhilippineHenry D. Mason, Tshwane University of Technology, South Africaİbrahim Yaşar Kazu, Firat University, TurkeyJane Liang, California Department of Education, USAJeyavel Sundaramoorthy, Gulbarga University Campus, IndiaJohn Cowan, Edinburgh Napier University, UKJonathan Chitiyo, University of Pittsburgh Bradford, USALinda J. Rappel, Yorkville University/University of Calgary, CanadaLorna T. Enerva, Polytechnic University of the Philippines, PhilippinesMary Sciaraffa, Eastern Kentucky University, USAMaurizio Sajeva, Pellervo Economic Research PTT, FinlandPuneet S. Gill, Texas A&M International University, USASandro Sehic, Oneida BOCES, USASayim Aktay, Mugla Sitki Kocman University, TurkeySelloane Pitikoe, University of Kwazulu-Natal, South AfricaSimona Savelli, Università degli Studi Guglielmo Marconi, ItalyStamatis Papadakis, University of Crete, GreeceYavuz Değirmenci, Bayburt University, Turkey 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".