Reviewer Acknowledgements for International Business Research, Vol. 10, No. 6
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 6 Ahmad Mahmoud Ahmad Zamil, King Saud University RCC, JordanAlina Badulescu, University of Oradea, RomaniaNasim Saadati, Panjab University, IndiaAmran Awang, Head of Entrepreneurship Center, MalaysiaAlireza Athari, Eastern Mediterranean University, IranSerhii Kozlovskiy, Donetsk National University, UkraineMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMongi Arfaoui, University of Monastir, TunisiaAurelija Burinskiene, Vilnius Gediminas Technical University, LithuaniaHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaGiuseppe Granata, University of Cassino and Southen Lazio, ItalyVincent Grèzes, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), SwitzerlandGianluca Ginesti, University of Naples “FEDERICO II”, ItalyAbedalqader Rababah, Arab Open University, OmanMuath Eleswed, American University of Kuwait, USAFrancesco Ciampi, Florence University, ItalyGeorgeta Dragomir, “Danubius” University of Galati, RomaniaFabio De Felice, University of Cassino and Southern Lazio, ItalyLadislav Mura, University of Ss. Cyril and Methodius in Trnava, SlovakiaMalgorzata Koszewska, Lodz University of Technology, PolandManlio Del Giudice, University of Rome "Link Campus", ItalyManuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş, RomaniaMaria do Céu Gaspar Alves, University of Beira Interior, PortugalMihaela Simionescu, Institute for Economic Forecasting of the Romanian Academy, RomaniaModar Abdullatif, Middle East University, JordanJorge Mongay-Hurtado, ESIC Business and Marketing School, SpainRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaTerrill Frantz, Peking University HSBC Business School, USAVassili JOANNIDES de LAUTOUR, Grenoble École de Management (France) and Queensland University of Technology School of Accountancy (Australia), FranceMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMaria J. Sanchez-Bueno, Universidad Carlos III se Madrid, SpainCheng Jing, eBay, Inc. / University of Rochester, USAArash Riasi, University of Delaware, USASumathisri Bhoopalan, Sastra University, IndiaFevzi Esen, Istanbul Medeniyet University, TurkeyAshford C Chea, Benedict College, USA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".