Reviewer Acknowledgements for International Business Research, Vol. 12, 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://www.ccsenet.org/journal/index.php/ibr/editor/recruitment and e-mail the completed application form to ibr@ccsenet.org. Reviewers for Volume 12, Number 2 Alina Badulescu, University of Oradea, Romania Alireza Athari, Eastern Mediterranean University, Iran Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Antonio Usai, University of Sassari, Italy Ashford C Chea, Benedict College, USA Aurelija Burinskiene, Vilnius Gediminas Technical University, Lithuania Celina Maria Olszak, University of Economics in Katowice, Poland Chokri Kooli, International Center for Basic Research applied, Paris, Canada Claudia Isac, University of Petrosani, Romania Cristian Marian Barbu, “ARTIFEX” University, Romania Cristian Rabanal, National University of Villa Mercedes, Argentina Dionito F. Mangao, Cavite State University – Naic Campus, Philippines Farouq Altahtamouni, Imam AbdulRahman Bin Fisal University, Jordan Federica Caboni, University of Cagliari, Italy Giuseppe Granata, University of Cassino and Southen Lazio, Italy Guy Baroaks, Ruppin academic center, Israel Haldun Şecaattin Çetinarslan, Turkish Naval Forces Command, Turkey Hanna Trojanowska, Warsaw University of Technology, Poland Henrique Fátima Boyol Ngan, Institute for Tourism Studies, Macao, Macao Hung-Che Wu, Nanfang College of Sun Yat-sen University, China Imran Riaz Malik, IQRA University, Pakistan Janusz Wielki, Opole University of Technology, Poland L. Leo Franklin, Bharathidasn University, India Lee Yok Yong, Universiti Putra Malaysia, Malaysia Marcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, Brazil Maria-Madela Abrudan, University of ORADEA, Romania Maryam Ebrahimi, Azad University, Iran Miriam Jankalová, University of Zilina, Slovakia Miroslav Iordanov Mateev, American University, Dubai, UAE Modar Abdullatif, Middle East University, Jordan Mohamed Abdel Rahman Salih, Taibah University, Saudi Arabia Mohsen Malekalketab Khiabani, University Technology Malaysia, Malaysia Muath Eleswed, American University of Kuwait, USA Murat Akin, Omer Halisdemir University FEAS – NIGDE, Turkey Pascal Stiefenhofer, University of Brighton, UK Rafiuddin Ahmed, James Cook University, Australia Razana Juhaida Johari, Universiti Teknologi MARA, Malaysia Riaz Ahsan, Government College University Faisalabad, Pakistan Riccardo Cimini, University of Tuscia, Viterbo, Italy Roxanne Helm Stevens, Azusa Pacific University, USA Serhii Kozlovskiy, Donetsk National University, Ukraine Tariq Tawfeeq Yousif Alabdullah, University of Basrah, Iraq Valeria Stefanelli, University of Salento, Italy Wanmo Koo, Western Illinois University, USA Wejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), Tunisia Wing- Keung Wong, Asia University, Taiwan, China Yasmin Tahira, Al Ain University of Science and Technology, Al Ain, UAE Zi-Yi Guo, Wells Fargo Bank, N.A., USA
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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 | 0.036 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.088 |
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".