Reviewer Acknowledgements for International Business Research, Vol. 12, No. 3
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 3 Alireza Athari, Eastern Mediterranean University, Iran Anca Gabriela Turtureanu, “DANUBIUS” University Galati, Romania Andrea Carosi, University of Sassari, Italy Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Antônio André Cunha Callado, Universidade Federal Rural de Pernmabuco, Brazil Ashford C Chea, Benedict College, USA Bruno Marsigalia, University of Casino and Southern Lazio, Italy Chokri Kooli, International Center for Basic Research applied, Paris, Canada Christopher Alozie, Tansian University, Nigeria Cristian Marian Barbu, “ARTIFEX” University, Romania Duminda Kuruppuarachchi, University of Otago, New Zealand Essia Ries Ahmed, Universiti Sains Malaysia, Malaysia Federica Caboni, University of Cagliari, Italy Federica De Santis, University of Pisa, Italy Florin Ionita, The Bucharest Academy of Economic Studies, Romania Foued Hamouda, Ecole Supérieure de Commerce, Tunisia Francesco Ciampi, Florence University, Italy Francesco Scalera, University of Bari "Aldo Moro", Italy Gianluca Ginesti, University of Naples “FEDERICO II”, Italy Hillary Odor, University of Benin, Nigeria Ivana Tomic, IT Company CloudTech, Republic of Serbia Joanna Katarzyna Blach, University of Economics in Katowice, Poland Joseph Lok-Man Lee, The Hong Kong Polytechnic University, Hong Kong Khaled Mokni, Northern Border University, Tunisia L. Leo Franklin, Bharathidasn University, India Ladislav Mura, University of Ss. Cyril and Methodius in Trnava, Slovakia Leow Hon Wei, SEGi University, Malaysia Manuel A. R. da Fonseca, Federal University of Rio de Janeiro (UFRJ), Brazil Marcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, Brazil Maria do Céu Gaspar Alves, University of Beira Interior, Portugal Maria Teresa Bianchi, University of Rome “LA SAPIENZA”, Italy Miriam Jankalová, University of Zilina, Slovakia Mongi Arfaoui, University of Monastir, Tunisia Muath Eleswed, American University of Kuwait, USA Ozgur Demirtas, Turkish Air Force Academy, Turkey Pascal Stiefenhofer, University of Brighton, UK Prosper Senyo Koto, Dalhousie University, Canada Rafiuddin Ahmed, James Cook University, Australia Razana Juhaida Johari, Universiti Teknologi MARA, Malaysia Riccardo Cimini, University of Tuscia, Viterbo, Italy Roberto Campos da Rocha Miranda, University Center Iesb, Brazil Sang- Bing Tsai, University of Electronic Science and Technology of China, China Sara Saggese, University of Naples Federico II, Italy Shun Mun Helen Wong, The Hong Kong Polytechnic University, Hong Kong Slavoljub M. Vujović, Economic Institute, Belgrade, Serbia Tariq Tawfeeq Yousif Alabdullah, University of Basrah, Iraq Valerija Botric, The Institute of Economics, Zagreb, Croatia Velia Gabriella Cenciarelli, University of Pisa, Italy Yan Lu, University of Central Florida, USA Yasmin Tahira, Al Ain University of Science and Technology, Al Ain, UAE
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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 | 0.033 | 0.287 |
| 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 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 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".