Reviewer Acknowledgements for Journal of Mathematics Research, Vol. 11, No. 3
Bibliographic record
Abstract
Journal of Mathematics Research 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 is greatly appreciated. Many authors, regardless of whether Journal of Mathematics Research publishes their work, appreciate the helpful feedback provided by the reviewers. Reviewers for Volume 11, Number 3 Abdessadek Saib, University of Tebessa, Algeria Arman Aghili, University of Guilan, Iran Cinzia Bisi, Ferrara University, Italy Gabriela Ciuperca, University Lyon 1, France Gener Santiago Subia, NUeva Ecija University of Science and Technology, Philippines Kong Liang, University of Illinois at Springfield, USA Kuldeep Narain Mathur, University Utara Malaysia, Malaysia Maria Alessandra Ragusa, University of Catania, Italy Rami Ahmad El, Athens Institute for Education and Research, Greece Rovshan Bandaliyev, National Academy of Sciences of Azerbaijan, Azerbaijan Sanjib Kumar Datta, University of Kalyani, India Shenghua Ni, Vanderbilt University Medical Center, USA Sreedhara Rao Gunakala, The University of The West Indies, Trinidad and Tobago Xiaofei Zhao, Texas A&M University, United States Yaqin Feng, Ohio University, USA Yifan Wang, University of Houston, USA Sophia Wang On behalf of, The Editorial Board of Journal of Mathematics Research Canadian Center of Science and Education
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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 | 0.041 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.079 | 0.044 |
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".