Reviewer Acknowledgements for International Business Research, Vol. 11, No. 2
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 2 Ahmad Mahmoud Ahmad Zamil, King Saud University RCC, JordanAlina Badulescu, University of Oradea, RomaniaAlireza Athari, Eastern Mediterranean University, IranAtallah Ahmad Alhosban , Aqaba University of Technology , JordanBadar Alam Iqbal, Aligarh Muslim University, SwitzerlandBenjamin James Inyang, University of Calabar, NigeriaBrian Sheehan, Thaksin University, ThailandBruno Ferreira Frascaroli, Federal University of Paraiba, BrazilCarlo Alberto Magni, University of Modena and Reggio Emilia, ItalyCheng Jing, eBay, Inc. / University of Rochester, USACristian Marian Barbu, “ARTIFEX” University, RomaniaEunju Lee, University of Massachusetts Lowell, USAFederica De Santis , University of Pisa , ItalyFilomena Izzo, University of Campania Luigi Vanvitelli, ItalyFlorin Ionita, The Bucharest Academy of Economic Studies, RomaniaGianluca Ginesti, University of Naples “FEDERICO II”, ItalyGilberto Marquez-Illescas, Clarkson University, USAGuo Zi-Yi, Wells Fargo Bank, N.A., USAHanna Trojanowska, Warsaw University of Technology, PolandHongliang Qiu, Tourism College of Zhejiang, ChinaHsiao-Ching Kuo, Washington and Jefferson College, USAHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaIonela-Corina Chersan, “Alexandru Ioan Cuza” University from Iași, RomaniaIsam Saleh, Al-Zaytoonah University of Jordan, JordanJolita Vveinhardt, Vytautas Magnus University, LithuaniaKaren 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 Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMaria-Madela Abrudan, University of ORADEA, RomaniaMichaela Maria Schaffhauser-Linzatti, University of Vienna, AustriaMiriam Jankalová, University of Zilina, SlovakiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMurat Akin, Omer Halisdemir University FEAS – NIGDE, TurkeyOnur Köprülü, Mersin University, TurkeyOzgur Demirtas, Turkish Air Force Academy, TurkeyRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaRosa Lombardi, Sapienza University of Rome, ItalyRoxanne Helm Stevens, Azusa Pacific University, USAShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongSumathisri Bhoopalan, SASTRA Deemed to be University, IndiaTariq Tawfeeq Yousif Alabdullah, University of Basrah, IraqValeria Stefanelli, University of Salento, ItalyVassili JOANNIDES de LAUTOUR, Grenoble École de Management (France) and Queensland University of Technology School of Accountancy (Australia), FranceYan 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 | 0.040 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.118 | 0.082 |
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".