Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 6, No. 6
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 6 Afsin Sahin, Gazi University, TurkeyBibi Abdelouahab, University Mentouri Constantine, AlgeriaCarla J. Thompson, University of West Florida, USAChin-Shang Li, University of California, Davis, CA, USAGabriel A. Okyere, Kwame Nkrumah University of Science and Technology, GhanaGane Samb Lo, University Gaston Berger, SenegalHui Zhang, St. Jude Children’s Research Hospital, USAIvair R. Silva, Federal University of Ouro Preto – UFOP, BrazilMan Fung LO, Hong Kong Polytechnic University, Hong KongNahid Sanjari Farsipour, Alzahra University, IranPablo José Moya Fernández, Universidad de Granada, SpainSamir Khaled Safi, The Islamic University of Gaza, PalestineShatrunjai Pratap Singh, John Hancock Financial Services, USASohair F. Higazi, University of Tanta, EgyptSubhradev Sen, Alliance University, IndiaTaehan Bae, University of Regina, CanadaTomás R. Cotos-Yáñez, University of Vigo, SpainVilda Purutcuoglu, Middle East Technical University (METU), TurkeyVyacheslav Abramov, Swinburne University of Technology, AustraliaWojciech Gamrot, University of Economics, PolandZaixing Li, China University of Mining and Technology (Beijing), China
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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 | 0.047 | 0.527 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 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".