Reviewer Acknowledgements for International Business Research, Vol. 11, No. 10
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 10 Andrea Carosi, University of Sassari, Italy Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Antônio André Cunha Callado, Universidade Federal Rural de Pernmabuco, Brazil Ashford C Chea, Benedict College, USA Aurelija Burinskiene, Vilnius Gediminas Technical University, Lithuania Benjamin James Inyang, University of Calabar, Nigeria Bruno Ferreira Frascaroli, Federal University of Paraiba, BrazilBrazil, Celina Maria Olszak, University of Economics in Katowice, Poland Cheng Jing, eBay, Inc. / University of Rochester, USA Chokri Kooli, International Center for Basic Research applied, Paris, Canada Claudia Isac, University of Petrosani, Romania Dea’a Al-Deen Al-Sraheen, Al-Zaytoonah University of Jordan , Jordan Eunju Lee, University of Massachusetts Lowell, USA Federica De Santis , University of Pisa , Italy Foued Hamouda, Ecole Supérieure de Commerce, Tunisia Francesco Ciampi, Florence University, Italy Gilberto Marquez-Illescas , University of Rhode Island, USA Giuseppe Granata, University of Cassino and Southen Lazio, Italy Giuseppe Russo, University of Cassino and Southern Lazio, Italy Guo Zi-Yi, Wells Fargo Bank, N.A., USA Imran Riaz Malik, IQRA University, Pakistan Janusz Wielki, Opole University of Technology, Poland Jerome Kueh, Universiti Malaysia Sarawak, Malaysia Joseph Lok-Man Lee, The Hong Kong Polytechnic University, Hong Kong Ladislav Mura, University of Ss. Cyril and Methodius in Trnava, Slovakia Luisa Pinto, University of Porto School of Economics, Portugal Manuel A. R. da Fonseca, Federal University of Rio de Janeiro (UFRJ), Brazil Manuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş, Romania Marcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, Brazil Maria-Madela Abrudan, University of ORADEA, Romania Maryam Ebrahimi, Azad University, Iran Mithat Turhan, Mersin University, Turkey Modar Abdullatif, Middle East University, Jordan Mohamed Abdel Rahman Salih, Taibah University, Saudi Arabia Ozgur Demirtas, Turkish Air Force Academy, Turkey Pascal Stiefenhofer, University of Brighton, UK Rafiuddin Ahmed, James Cook University, Australia Riaz Ahsan, Government College University Faisalabad, Pakistan Sumathisri Bhoopalan, SASTRA Deemed to be University, India Valeria Stefanelli, University of Salento, Italy Valerija Botric, The Institute of Economics, Zagreb, Croatia Wanmo Koo, Western Illinois University, USA Wejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), Tunisia 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 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.129 | 0.093 |
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".