Reviewer Acknowledgements for International Business Research, Vol. 11, No. 8
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 8 Alireza Athari, Eastern Mediterranean University, IranAnca Gabriela Turtureanu, “DANUBIUS” University Galati, RomaniaAndrea Carosi, University of Sassari, ItalyCheng Jing, eBay, Inc. / University of Rochester, USAChokri Kooli, International Center for Basic Research applied, Paris, CanadaDaniel Cash, Aston University, United KingdomDonghun Yoon, Seoul Center, Korea Basic Science Institute, Republic of KoreaFilomena Izzo, University of Campania Luigi Vanvitelli, ItalyFrancesco Ciampi, Florence University, ItalyGeorgeta Dragomir, “Danubius” University of Galati, RomaniaGiuseppe Granata, University of Cassino and Southen Lazio, ItalyGuo Zi-Yi, Wells Fargo Bank, N.A., USAHanna Trojanowska, Warsaw University of Technology, PolandIonela-Corina Chersan, “Alexandru Ioan Cuza” University from Iași, RomaniaJoseph Lok-Man Lee, The Hong Kong Polytechnic University, Hong KongKaren Gulliver, Argosy University, Twin Cities, USALadislav Mura, University of Ss. Cyril and Methodius in Trnava, SlovakiaM. Muzamil Naqshbandi, University of Dubai, UAEMarcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, BrazilMaria-Madela Abrudan, University of ORADEA, RomaniaMaryam Ebrahimi, Azad University, IranMichaela Maria Schaffhauser-Linzatti, University of Vienna, AustriaMichele Rubino, Università LUM Jean Monnet, ItalyMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMongi Arfaoui, University of Monastir, TunisiaMuath Eleswed, American University of Kuwait, USAOnur Köprülü, Mersin University, TurkeyPascal Stiefenhofer, University of Brighton, UKRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaStephen Donald Strombeck, William Jessup University, USAValeria Stefanelli, University of Salento, ItalyWanmo Koo, Western Illinois University, USAWejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), TunisiaYan Lu, University of Central Florida, USAYasmin 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 | 0.037 | 0.328 |
| 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) | 0.118 | 0.080 |
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".