Reviewer Acknowledgements for International Business Research, Vol. 11, No. 11
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 11 Alireza Athari, Eastern Mediterranean University, Iran Anca Gabriela Turtureanu, “DANUBIUS” University Galati, Romania Andrea Carosi, University of Sassari, Italy Andrei Buiga, “ARTIFEX University of Bucharest, Romania Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Antônio André Cunha Callado, Universidade Federal Rural de Pernmabuco, Brazil Antonio Usai, University of Sassari, Italy Ashford C Chea, Benedict College, USA Bazeet Olayemi Badru, Universiti Utara Malaysia, Nigeria Chokri Kooli, International Center for Basic Research applied, Paris, Canada Duminda Kuruppuarachchi, University of Otago, New Zealand Essia Ries Ahmed, Universiti Sains Malaysia, Malaysia Fevzi Esen, Istanbul Medeniyet University, Turkey Filomena Izzo, University of Campania Luigi Vanvitelli, Italy Francesco Scalera, University of Bari "Aldo Moro", Italy Grzegorz Zasuwa, The John Paul II Catholic University of Lublin, Poland Haldun Şecaattin Çetinarslan, Turkish Naval Forces Command, Turkey Hanna Trojanowska, Warsaw University of Technology, Poland Herald Monis, Milagres College, India Hsiao-Ching Kuo, Washington and Jefferson College, USA Hung-Che Wu, Nanfang College of Sun Yat-sen University, China Ionela-Corina Chersan, “Alexandru Ioan Cuza” University from Iași, Romania Iwona Gorzeń-Mitka, Czestochowa University of Technology, Poland Janusz Wielki, Opole University of Technology, Poland Keshmeer Makun, University o the South Pacific, Fiji Khaled Mokni, Northern Border University, Tunisia L. Leo Franklin, Bharathidasn University, India Luisa Pinto, University of Porto School of Economics, Portugal Mahdi Shadkam, University Technology Malaysia, 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 Teresa Bianchi, University of Rome “LA SAPIENZA”, Italy Michaela Maria Schaffhauser-Linzatti, University of Vienna, Austria Miriam Jankalová, University of Zilina, Slovakia Miroslav Iordanov Mateev, American University, Dubai, UAE Mithat Turhan, Mersin University, Turkey Mohsen Malekalketab Khiabani, University Technology Malaysia, Malaysia Muath Eleswed, American University of Kuwait, USA Murat Akin, Omer Halisdemir University FEAS – NIGDE, Turkey Ozgur Demirtas, Turkish Air Force Academy, Turkey Radoslav Jankal, University of Zilina, Slovakia Riaz Ahsan, Government College University Faisalabad, Pakistan Roxanne Helm Stevens, Azusa Pacific University, USA Serhii Kozlovskiy, Donetsk National University, Ukraine Shun Mun Helen Wong, The Hong Kong Polytechnic University, Hong Kong Wanmo Koo, Western Illinois University, 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 | 0.035 | 0.298 |
| 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 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.118 | 0.085 |
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".