Reviewer Acknowledgements for International Business Research, Vol. 12, No. 1
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 1 Abedalqader Rababah, Arab Open University, Oman Ajit Kumar Kar, Indian Metal & Ferro Alloys Ltd, Bhubaneswar, Odisha, India Alireza Athari, Eastern Mediterranean University, Iran Andrei Buiga, “ARTIFEX University of Bucharest, Romania Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Ashford C Chea, Benedict College, USA Aurelija Burinskiene, Vilnius Gediminas Technical University, Lithuania Bazeet Olayemi Badru, Universiti Utara Malaysia, Nigeria Benjamin James Inyang, University of Calabar, Nigeria Celina Maria Olszak, University of Economics in Katowice, Poland Claudia Isac, University of Petrosani, Romania Dionito F. Mangao, Cavite State University – Naic Campus, Philippines Duminda Kuruppuarachchi, University of Otago, New Zealand Federica Caboni, University of Cagliari, Italy Federica De Santis, University of Pisa, Italy Georges Samara, ESADE Business School, Lebanon Gianluca Ginesti, University of Naples “FEDERICO II”, Italy Gilberto MarquezIllescas, University of Rhode Island, USA Guo ZiYi, Wells Fargo Bank, N.A., USA Hejun Zhuang, Brandon University, Canada Henrique Fátima Boyol Ngan, Institute for Tourism Studies, Macao, Macao Herald Monis, Milagres College, India HungChe Wu, Nanfang College of Sun Yatsen University, China Joanna Katarzyna Blach, University of Economics in Katowice, Poland 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 MariaMadela Abrudan, University of ORADEA, Romania Marta Joanna Ziólkowska, Warsaw School of Economics (Szkoła Główna Handlowa), Poland Maryam Ebrahimi, Azad University, Iran Michaela Maria SchaffhauserLinzatti, University of Vienna, Austria Michele Rubino, Università LUM Jean Monnet, Italy Mithat Turhan, Mersin University, Turkey Mohsen Malekalketab Khiabani, University Technology Malaysia, Malaysia Mongi Arfaoui, University of Monastir, Tunisia Murat Akin, Omer Halisdemir University FEAS – NIGDE, Turkey Ozgur Demirtas, Turkish Air Force Academy, Turkey Pascal Stiefenhofer, University of Brighton, UK Roxanne Helm Stevens, Azusa Pacific University, USA Sara Saggese, University of Naples Federico II, Italy Serhii Kozlovskiy, Donetsk National University, Ukraine Shame Mukoka, Zimbabwe Open University, Zimbabwe Shun Mun Helen Wong, The Hong Kong Polytechnic University, Hong Kong Silvia Ferramosca, University of Pisa, Italy Sumathisri Bhoopalan, SASTRA Deemed to be University, India Tatiana Marceda Bach, Centro Universitário Univel (UNIVEL), Brazil Vassili JOANNIDES de LAUTOUR, Grenoble École de Management (France) and Queensland University of Technology School of Accountancy (Australia), France Wanmo Koo, Western Illinois University, USA Wasilu Suleiman, Bauchi State University, Nigeria Wejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), Tunisia
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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 | 0.037 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.129 | 0.095 |
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".