Reviewer Acknowledgements for International Business Research, Vol. 10, No. 4
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 4 Abderrazek Hassen Elkhaldi, University of Sousse, TunisiaAlina Badulescu, University of Oradea, RomaniaAlireza Athari, Eastern Mediterranean University, IranAmaresh C. Das, Southern University at New Orleans, USAAmran Awang, Head of Entrepreneurship Center, MalaysiaAndrea Carosi, University of Sassari, ItalyAnna Paola Micheli, Univrtsity of Cassino and Southern Lazio, ItalyAntônio André Cunha Callado, Universidade Federal Rural de Pernmabuco, BrazilArash Riasi, University of Delaware, USAAshford C Chea, Benedict College, USABenjamin James Inyang, University of Calabar, NigeriaBrian Sheehan, Thaksin University, AustraliaBruno Marsigalia, University of Casino and Southern Lazio, ItalyCheng Jing, eBay, Inc. / University of Rochester, USACristian Marian Barbu, “ARTIFEX” University, RomaniaEva Mira Bolfíková, Univerzity of P. J. Šafárik in Košice, Slovak Republic,Fevzi Esen, Istanbul Medeniyet University, TurkeyFrancesco Ciampi, Florence University, ItalyGeorgeta Dragomir, “Danubius” University of Galati, RomaniaGianluca Ginesti, University of Naples “FEDERICO II”, ItalyGiuseppe Russo, University of Cassino and Southern Lazio, ItalyHanna Trojanowska, Warsaw University of Technology, PolandHerald Monis, Milagres College, IndiaIvo De Loo, Nyenrode Business University, The NetherlandsKaren Gulliver, Argosy University, Twin Cities, USAKherchi Ishak, University of Hassiba Ben Bouali De Chlef, AlgeriaLadislav Mura, University of Ss. Cyril and Methodius in Trnava, SlovakiaLuisa Pinto, University of Porto School of Economics, PortugalMansour Esmaeil Zaei, Panjab University, India/IranManuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş Romania, RomaniaMarcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, BrazilMaria Teresa Bianchi, UNIVERSITY OF ROME “LA SAPIENZA”, ItalyMiriam Jankalová, University of Zilina, SlovakiaMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMongi Arfaoui, University of Monastir, TunisiaMuath Eleswed, American University of Kuwait, USAOzgur Demirtas, Turkish Air Force Academy, TurkeyProsper Senyo Koto, Dalhousie University, CanadaRadoslav Jankal, University of Zilina, SlovakiaRoberto Campos da Rocha Miranda, University Center Iesb, BrazilRosa Lombardi, Sapienza University of Rome, ItalySerhii Kozlovskiy, Donetsk National University, UkraineSumathisri Bhoopalan, Sastra University, IndiaWejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), TunisiaWing-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 | 0.037 | 0.324 |
| 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 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.115 | 0.076 |
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".