Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 6, 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 6, Number 3 Ali Reza Fotouhi, University of the Fraser Valley, CanadaChin-Shang Li, University of California, USADouglas Lorenz, University of Louisville, USAFarida Kachapova, The Auckland University of Technology, New ZealandFelix Almendra-Arao, UPIITA del Instituto Politécnico Nacional, MéxicoGane Samb Lo, University Gaston Berger, SenegalGerardo Febres, Universidad Simón Bolívar, VenezuelaHaiming Zhou, Northern Illinois University, USAHui Zhang, St. Jude Children’s Research Hospital, USAJacek Białek, University of Lodz, PolandLuiz Ricardo Nakamura, University of Sao Paulo, BrazilMarcelo Bourguignon, Universidade Federal de Pernambuco, BrazilMaryam Eskandarzadeh, Persion Gulf Boshehr University, IranNahid Sanjari Farsipour, Alzahra University, IranPhilip Westgate, University of Kentucky, USARebecca Bendayan, University College London, UKSajid Ali, Bocconi University, ItalyShatrunjai Pratap Singh, John Hancock Financial Services, USAShuling Liu, Yale University, USASohair F. Higazi, University of Tanta, EgyptSubhradev Sen, Alliance University, IndiaTomás R. Cotos-Yáñez, University of Vigo, SpainVyacheslav Abramov, Swinburne University of Technology, AustraliaZaixing Li, China University of Mining and Technology (Beijing), China Wendy SmithOn behalf of,The Editorial Board of International Journal of Statistics and ProbabilityCanadian 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 | 0.050 | 0.528 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.074 | 0.040 |
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".