Reviewer Acknowledgements for International Business Research, Vol. 11, No. 9
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 9 Abderrazek Hassen Elkhaldi, University of Sousse, TunisiaAlireza Athari, Eastern Mediterranean University, IranAnca Gabriela Turtureanu, “DANUBIUS” University Galati, RomaniaAndrei Buiga, “ARTIFEX University of Bucharest, RomaniaAnna Paola Micheli, Univrtsity of Cassino and Southern Lazio, ItalyBenjamin James Inyang, University of Calabar, NigeriaChokri Kooli, International Center for Basic Research applied, Paris, CanadaClaudia Isac, University of Petrosani, RomaniaDaniel Cash, Aston University, United KingdomDonghun Yoon, Seoul Center, Korea Basic Science Institute, Republic of KoreaEwa Ziemba, University of Economics in Katowice, PolandFawzieh Mohammed Masad, Jadara University, JordanFederica Caboni, University of Cagliari, ItalyFlorin Ionita, The Bucharest Academy of Economic Studies, RomaniaFoued Hamouda, Ecole Supérieure de Commerce, TunisiaFrancesco Ciampi, Florence University, ItalyGeorges Samara, ESADE Business School, LebanonGeorgeta Dragomir, “Danubius” University of Galati, RomaniaHaldun Şecaattin Çetinarslan, Turkish Naval Forces Command, TurkeyHanna Trojanowska, Warsaw University of Technology, PolandImran Riaz Malik, IQRA University, PakistanJanusz Wielki, Opole University of Technology, PolandL. Leo Franklin, Bharathidasn University, IndiaM. Muzamil Naqshbandi, University of Dubai, UAEMarcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, BrazilMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMichele Rubino, Università LUM Jean Monnet , ItalyMihaela Simionescu, Institute for Economic Forecasting of the Romanian Academy, RomaniaMiriam Jankalová, University of Zilina, SlovakiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMuath Eleswed, American University of Kuwait, USANadia Oliva, Telematic University Giustino Fortunato, ItalyOzgur Demirtas, Turkish Air Force Academy, TurkeyProsper Senyo Koto, Dalhousie University, CanadaRafael Hernandez Barros, Universidad Complutense de Madrid, SpainRiccardo Cimini, University of Tuscia, Viterbo, ItalyRoxanne Helm Stevens, Azusa Pacific University, USASerhii Kozlovskiy, Donetsk National University, UkraineShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongSilvia Ferramosca, University of Pisa, ItalySumathisri Bhoopalan, SASTRA Deemed to be University, IndiaTariq Tawfeeq Yousif Alabdullah, University of Basrah, IraqValeria Stefanelli, University of Salento, ItalyYan Lu, University of Central Florida, USAYasmin Tahira, Al Ain University of Science and Technology, Al Ain, UAE
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".