Reviewer Acknowledgements for International Business Research, Vol. 14, 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 contact us for the application form at: ibr@ccsenet.org Reviewers for Volume 14, Number 12 Ahnaf Ali Alsmady, University of Tabuk, Saudi Arabia Anca Gabriela Turtureanu, “DANUBIUS” University Galati, Romania Anna Maria Calce, University of Cassino and Southern Lazio, Italy Benjamin James Inyang, University of Calabar, Nigeria Bruno Ferreira Frascaroli, Federal University of Paraiba, Brazil Chokri Kooli, International Center for Basic Research applied, Paris, Canada Chuan Huat Ong, SEGi University Kota Damansara, Malaysia Chunyu Zhang, Guangxi Normal University, China Cristian Marian Barbu, “ARTIFEX” University, Romania Francesco Scalera, University of Bari "Aldo Moro", Italy Giuseppe Granata, University Mercatorum of Rome, Italy Gnahe franck E, JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS, COTE D’IVOIRE Hanna Trojanowska, Warsaw University of Technology, Poland Henrique Fátima Boyol Ngan, Institute for Tourism Studies, Macao, Macao Hind Ahmed, Ahfad university for Women, Sudan Ivano De Turi, LUM Jean Monnet University, Italy Janusz Wielki, Opole University of Technology, Poland L. Leo Franklin, Bharathidasn University, India Ladislav Mura, University of Ss. Cyril and Methodius in Trnava, Slovakia Lee Yok Yong, Universiti Putra Malaysia, Malaysia Leow Hon Wei, SEGi University, Malaysia MALIK ELHAJ, University of Pittsburgh at Bradford, USA Marcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, Brazil Marco Valeri, Niccolò Cusano University, Italy Maria-Madela Abrudan, University of ORADEA, Romania Mohammad S. Knio, City University College of Ajman, UK Mustafa Özer, Anadolu University, FEAS, Turkey Omer Allagabo Omer Mustafa, Sudan Academy for Banking and Financial Sciences, Sudan Pascal Stiefenhofer, University of Exeter, UK Rosemary Boateng Coffie, Kwame Nkrumah University of Science and |Technology, Ghana Roxanne Helm Stevens, Azusa Pacific University, USA Sachita Yadav, Arun Jaitley National Institute of Financial Management, India Sara Saggese, University of Naples Federico II, Italy Shrijan Gyanwali, Pokhara University, Nepal Stoyan Neychev, University of National and World Economy, Bulgaria Sumathisri Bhoopalan, SASTRA Deemed to be University, India Wanmo Koo, Western Illinois University, USA 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 | 0.031 | 0.284 |
| 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) | 0.122 | 0.088 |
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".