Reviewer Acknowledgements for International Business Research, Vol. 10, No. 7
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 10, Number 7 Abedalqader Rababah, Arab Open University, OmanAlina Badulescu, University of Oradea, RomaniaAlireza Athari, Eastern Mediterranean University, IranAmran Awang, Head of Entrepreneurship Center, MalaysiaAnca Gabriela Turtureanu, “DANUBIUS” University Galati, RomaniaAndrea Carosi, University of Sassari, ItalyAnna Paola Micheli, Univrtsity of Cassino and Southern Lazio, ItalyArash Riasi, University of Delaware, USAAshford C Chea, Benedict College, USABenjamin James Inyang, University of Calabar, NigeriaCheng Jing, eBay, Inc. / University of Rochester, USACristian Marian Barbu, “ARTIFEX” University, RomaniaGilberto Marquez-Illescas , Clarkson University , USAGiuseppe Granata, University of Cassino and Southen Lazio, 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, ChinaIonela-Corina Chersan, “Alexandru Ioan Cuza” University from Iași, RomaniaJorge Mongay-Hurtado, ESIC Business and Marketing School, SpainKaren Gulliver, Argosy University, Twin Cities, USAManlio Del Giudice, University of Rome "Link Campus", ItalyMaria do Céu Gaspar Alves, University of Beira Interior, PortugalMaria J. Sanchez-Bueno, Universidad Carlos III se Madrid, SpainMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMaria-Madela Abrudan, University of ORADEA, RomaniaMiriam Jankalová, University of Zilina, SlovakiaMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMongi Arfaoui, University of Monastir, TunisiaOzgur Demirtas, Turkish Air Force Academy, TurkeyRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaRaphaël Dornier, Université Savoie Mont Blanc, FranceRoberto Campos da Rocha Miranda, University Center Iesb, BrazilRoxanne Helm Stevens, Azusa Pacific University, USASang-Bing Tsai, University of Electronic Science and Technology of China, ChinaValeria Stefanelli, Università degli Studi Niccolò Cusano, ItalyVassili JOANNIDES de LAUTOUR, Grenoble École de Management (France) and Queensland University of Technology School of Accountancy (Australia), FranceVincent Grèzes, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), SwitzerlandYan 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 | 0.036 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.114 | 0.079 |
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".