Reviewer Acknowledgements for International Business Research, Vol. 11, 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://www.ccsenet.org/journal/index.php/ibr/editor/recruitment and e-mail the completed application form to ibr@ccsenet.org. Reviewers for Volume 11, Number 12 Abderrazek Hassen Elkhaldi, University of Sousse, Tunisia Ajit Kumar Kar, Indian Metal & Ferro Alloys Ltd, Bhubaneswar, Odisha, India Alina Badulescu, University of Oradea, Romania Anca Gabriela Turtureanu, “DANUBIUS” University Galati, Romania Andrea Carosi, University of Sassari, Italy Andrei Buiga, “ARTIFEX University of Bucharest, Romania Antonio Usai, University of Sassari, Italy Ashford C Chea, Benedict College, USA Celina Maria Olszak, University of Economics in Katowice, Poland Chemah Tamby Chik, Universiti Teknologi Mara (Uitm), Malaysia Christos Chalyvidis, Hellenic Air Force Academy, Greece Cristian Rabanal, National University of Villa Mercedes, Argentina Duminda Kuruppuarachchi, University of Otago, New Zealand Federica Caboni, University of Cagliari, Italy Federica De Santis , University of Pisa , Italy Fevzi Esen, Istanbul Medeniyet University, Turkey Filomena Izzo, University of Campania Luigi Vanvitelli, Italy Florin Ionita, The Bucharest Academy of Economic Studies, Romania Francesco Scalera, University of Bari "Aldo Moro", Italy Georges Samara, ESADE Business School, Lebanon Giuseppe Granata, University of Cassino and Southen Lazio, Italy Hanna Trojanowska, Warsaw University of Technology, Poland Hejun Zhuang, Brandon University, Canada Imran Riaz Malik, IQRA University, Pakistan Ionela-Corina Chersan, “Alexandru Ioan Cuza” University from Iași, Romania Isam Saleh, Al-Zaytoonah University of Jordan, Jordan Joseph Lok-Man Lee, The Hong Kong Polytechnic University, Hong Kong Khaled Mokni, Northern Border University, Tunisia L. Leo Franklin, Bharathidasn University, India M. Muzamil Naqshbandi, University of Dubai, UAE Marcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, Brazil Maria Teresa Bianchi, University of Rome “LA SAPIENZA”, Italy Michele Rubino, Università LUM Jean Monnet, Italy Miriam Jankalová, University of Zilina, Slovakia Mohamed Abdel Rahman Salih, Taibah University, Saudi Arabia Mongi Arfaoui, University of Monastir, Tunisia Muath Eleswed, American University of Kuwait, USA Ozgur Demirtas, Turkish Air Force Academy, Turkey Prosper Senyo Koto, Dalhousie University, Canada Radoslav Jankal, University of Zilina, Slovakia Rafiuddin Ahmed, James Cook University, Australia Riaz Ahsan, Government College University Faisalabad, Pakistan Roxanne Helm Stevens, Azusa Pacific University, USA Sang-Bing Tsai, University of Electronic Science and Technology of China, China Sara Saggese, University of Naples Federico II, Italy Sumathisri Bhoopalan, SASTRA Deemed to be University, India Wejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), Tunisia 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 | 0.037 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.131 | 0.101 |
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".