Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 8, No. 3
Bibliographic record
Abstract
International Journal of Statistics and Probability 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 International Journal of Statistics and Probability publishes their work, appreciate the helpful feedback provided by the reviewers. Reviewers for Volume 8, Number 3 Abdullah A. Smadi, Yarmouk University, Jordan Carla J. Thompson, University of West Florida, USA Carolyn Huston, Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia Faisal Khamis, Al Ain University of Science and Technology, Canada Felix Almendra-Arao, UPIITA del Instituto Politécnico Nacional , México Gane Samb Lo, University Gaston Berger, SENEGAL Gennaro Punzo, University of Naples Parthenope, Italy Gerardo Febres, Universidad Simón Bolívar, Venezuela Jacek Białek, University of Lodz, Poland Kassim S. Mwitondi, Sheffield Hallam University, UK Krishna K. Saha, Central Connecticut State University, USA Man Fung LO, Hong Kong Polytechnic University, Hong Kong Marcelo Bourguignon, Universidade Federal de Pernambuco, Brazil Mingao Yuan, North Dakota State University, USA Mohieddine Rahmouni, University of Tunis, Tunisia Nahid Sanjari Farsipour, Alzahra University, Iran Noha Youssef, American University in Cairo, Egypt Pablo José Moya Fernández, Universidad de Granada, Spain Philip Westgate, University of Kentucky, USA Shatrunjai Pratap Singh, John Hancock Financial Services, USA Sohair F. Higazi, University of Tanta, Egypt Vilda Purutcuoglu, Middle East Technical University (METU), Turkey Vyacheslav Abramov, Swinburne University of Technology, Australia Wei Zhang, The George Washington University, USA Zaixing Li, China University of Mining and Technology (Beijing), China Wendy Smith On behalf of, The Editorial Board of International Journal of Statistics and Probability 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 | 0.044 | 0.455 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.081 | 0.050 |
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".