Reviewer Acknowledgements for International Business Research, Vol. 11, No. 5
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://recruitment.ccsenet.org and e-mail the completed application form to ibr@ccsenet.org.Reviewers for Volume 11, Number 5 Abderrazek Hassen Elkhaldi, University of Sousse, TunisiaAnna Paola Micheli, Univrtsity of Cassino and Southern Lazio, ItalyAurelija Burinskiene, Vilnius Gediminas Technical University, LithuaniaCelina Maria Olszak, University of Economics in Katowice, PolandFawzieh Mohammed Masad, Jadara University, JordanFederica De Santis, University of Pisa , ItalyFevzi Esen, Istanbul Medeniyet University, TurkeyFilomena Izzo, University of Campania Luigi Vanvitelli, ItalyFlorin Ionita, The Bucharest Academy of Economic Studies, RomaniaFrancesco Ciampi, Florence University, ItalyFrancesco Scalera, University of Bari "Aldo Moro", ItalyGrzegorz Zasuwa, The John Paul II Catholic University of Lublin, PolandHanna Trojanowska, Warsaw University of Technology, PolandHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaImran Riaz Malik, IQRA University, PakistanJorge Mongay-Hurtado, ESIC Business and Marketing School, SpainKaren Gulliver, Argosy University, Twin Cities, USAM. Muzamil Naqshbandi, University of Dubai, UAEMaria do Céu Gaspar Alves, University of Beira Interior, PortugalMaurizio Rija, University of Calabria, ItalyMihaela Simionescu, Institute for Economic Forecasting of the Romanian Academy, RomaniaModar Abdullatif, Middle East University, JordanMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMuath Eleswed, American University of Kuwait, USAMurat Akin, Omer Halisdemir University FEAS – NIGDE, TurkeyÖzcan IŞIK, Cumhuriyet University, TurkeyPascal Stiefenhofer, University of Brighton, UKProsper Senyo Koto, Dalhousie University, CanadaRadoslav Jankal, University of Zilina, SlovakiaRiccardo Cimini, University of Tuscia, Viterbo, ItalyRoberto Campos da Rocha Miranda, University Center Iesb, BrazilShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongValeria Stefanelli, University of Salento, ItalyVincent Grèzes, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), SwitzerlandWanmo Koo, Western Illinois University, USAWing-Keung Wong, Asia University, Taiwan, China
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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 | 0.043 | 0.341 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.136 | 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".