Reviewer Acknowledgements for International Business Research, Vol. 10, No. 12
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 12Abedalqader Rababah, Arab Open University, OmanAhmad Mahmoud Ahmad Zamil, King Saud University RCC, JordanAlireza Athari, Eastern Mediterranean University, IranAnca Gabriela Turtureanu, “DANUBIUS” University Galati, RomaniaAnna Paola Micheli, Univrtsity of Cassino and Southern Lazio, ItalyAntonella Petrillo, University of Napoli “Parthenope”, ItalyAshford C Chea, Benedict College, USAAtallah Ahmad Alhosban, Aqaba University of Technology, JordanBenjamin James Inyang, University of Calabar, NigeriaCheng Jing, eBay, Inc. / University of Rochester, USAChuan Huat Ong, KDU Penang University College, MalaysiaCristian Marian Barbu, “ARTIFEX” University, RomaniaFederica De Santis, University of Pisa, ItalyFoued Hamouda, Ecole Supérieure de Commerce, TunisiaFrancesco Ciampi, Florence University, ItalyFrancesco Scalera, University of Bari "Aldo Moro", ItalyGrzegorz Zasuwa, The John Paul II Catholic University of Lublin, PolandGuillaume Marceau, University of Aix-Marseille, FranceHanna Trojanowska, Warsaw University of Technology, PolandHerald Monis, Milagres College, IndiaHongliang Qiu, Tourism College of Zhejiang, ChinaHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaJanusz Wielki, University of Business in Wroclaw, PolandKherchi Ishak, University of Hassiba Ben Bouali De Chlef, AlgeriaLadislav Mura, University of Ss. Cyril and Methodius in Trnava, SlovakiaMahdi Shadkam, University Technology Malaysia, MalaysiaManuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş, RomaniaMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMaria-Madela Abrudan, University of ORADEA, RomaniaMiriam Jankalová, University of Zilina, SlovakiaMiroslav Iordanov Mateev, American University, Dubai, UAEMithat Turhan, Mersin University, TurkeyModar Abdullatif, Middle East University, JordanMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMongi Arfaoui, University of Monastir, TunisiaMuath Eleswed, American University of Kuwait, USAOnur Köprülü, Mersin University, TurkeyÖzcan IŞIK, Cumhuriyet University, TurkeyPascal Stiefenhofer, University of Brighton, UKRadoslav Jankal, University of Zilina, SlovakiaSang-Bing Tsai, University of Electronic Science and Technology of China, ChinaShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongValeria Stefanelli, Università del Salento, Italy
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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 | 0.040 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.116 | 0.084 |
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".