Reviewer Acknowledgements for International Business Research, Vol. 10, No. 2
Bibliographic record
Abstract
International Business 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 are greatly appreciated.International Business Research is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please find the application form and details at http://recruitment.ccsenet.org and e-mail the completed application form to ibr@ccsenet.org.Reviewers for Volume 10, Number 2 Ahmad Mahmoud Ahmad Zamil, King Saud University RCC, JordanAlireza Athari, Eastern Mediterranean University, IranAmran Awang, Head of Entrepreneurship Center, MalaysiaAshford C Chea, Benedict College, USABadar Nadeem Ashraf, East China Jiao Tong University, ChinaBenjamin James Inyang, University of Calabar, NigeriaBrian Sheehan, Thaksin University, AustraliaBruno Marsigalia, University of Casino and Southern Lazio, ItalyCristian Marian Barbu, “ARTIFEX” University, RomaniaFrancesco Ciampi, Florence University, ItalyGiuseppe Granata, University of Cassino and Southen Lazio, ItalyGiuseppe Russo, University of Cassino and Southern Lazio, ItalyHanna Trojanowska, Siedlce University, PolandHerald Monis, Milagres College, IndiaHuijian Dong, Pacific University, USAIvo De Loo, Nyenrode Business University, The NetherlandsIwona Gorzeń-Mitka, Czestochowa University of Technology, PolandJanusz Wielki, University of Business in Wroclaw, PolandL. Leo Franklin, Bharathidasn University, IndiaMansour Esmaeil Zaei, Panjab University, IndiaMarcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, BrazilMaryam Ebrahimi, Azad University, IranMichaela Maria Schaffhauser-Linzatti, University of Vienna, AustriaMihaela Simionescu, Bucharest Academy of Economic Studies, RomanianModar Abdullatif, Middle East University, JordanMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMongi Arfaoui, University of Monastir, TunisiaPascal Stiefenhofer, University of Brighton, UKProsper Senyo Koto, Dalhousie University, CanadaRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaSang-Bing Tsai, University of Electronic Science and Technology of China, ChinaTu Anh Phan, CanTho University, Viet Nam
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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 | 0.349 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.112 | 0.074 |
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".