Reviewer Acknowledgements for International Business Research, Vol. 12, No. 4
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 4 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 Bazeet Olayemi Badru, Universiti Utara Malaysia, Nigeria Bruno Ferreira Frascaroli, Federal University of Paraiba, Brazil Celina Maria Olszak, University of Economics in Katowice, Poland Christopher Alozie, Tansian University, Nigeria Cristian Rabanal, National University of Villa Mercedes, Argentina Francesco Ciampi, Florence University, Italy Francesco Scalera, University of Bari "Aldo Moro", Italy 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 Herald Monis, Milagres College, India Hillary Odor, University of Benin, Nigeria Imran Riaz Malik, IQRA University, Pakistan L. Leo Franklin, Bharathidasn University, India Ladislav Mura, University of Ss. Cyril and Methodius in Trnava, Slovakia Leow Hon Wei, SEGi University, Malaysia Luisa Pinto, University of Porto School of Economics, Portugal M- Muzamil Naqshbandi, University of Dubai, UAE 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 Marco Valeri, Niccolò Cusano University, Italy Marta Joanna Ziólkowska, Warsaw School of Economics (Szkoła Główna Handlowa), Poland Michele Rubino, Università LUM Jean Monnet, Italy Mohamed Abdel Rahman Salih, Taibah University, Saudi Arabia Mohsen Malekalketab Khiabani, University Technology Malaysia, Malaysia Muath Eleswed, American University of Kuwait, USA Nicoleta Barbuta-Misu, “Dunarea de Jos” University of Galati, Romania Ozgur Demirtas, Turkish Air Force Academy, Turkey Pascal Stiefenhofer, University of Brighton, UK Radoslav Jankal, University of Zilina, Slovakia Razana Juhaida Johari, Universiti Teknologi MARA, Malaysia Riaz Ahsan, Government College University Faisalabad, Pakistan Roxanne Helm Stevens, Azusa Pacific University, USA Serhii Kozlovskiy, Donetsk National University, Ukraine Slavoljub M. Vujović, Economic Institute, Belgrade, Serbia Stephen Donald Strombeck, William Jessup University, USA 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
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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 | 0.032 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.123 | 0.087 |
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".