Reviewer Acknowledgements for International Business Research, Vol. 10, No. 3
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 3 Ajit Kumar Kar, WOMS India, IndiaAlireza Athari, Eastern Mediterranean University, IranAmran Awang, Head of Entrepreneurship Center, MalaysiaAnca Gabriela Turtureanu, “DANUBIUS” University Galati, RomaniaAntônio 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, AustraliaCelina Maria Olszak, University of Economics in Katowice, PolandCheng Jing, eBay, Inc. / University of Rochester, USAEmilio Congregado, University of Huelva, SpainFevzi Esen, Istanbul Medeniyet University, TurkeyFlorin Ionita, The Bucharest Academy of Economic Studies, Romania,Francesco Ciampi, Florence University, ItalyGiovanna Michelon, University of Padova, ItalyGrzegorz Strupczewski, Cracow University of Economics, PolandGrzegorz Zasuwa, The John Paul II Catholic University of Lublin, PolandHanna Trojanowska, Warsaw University of Technology, PolandHuijian Dong, Pacific University, USAL. Leo Franklin, Bharathidasn University, IndiaM. Muzamil Naqshbandi, University of Dubai, UAEMansour Esmaeil Zaei, Panjab University, India/IranManuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş Romania, RomaniaMaria João Guedes, University of Lisbon, PortugalMaria-Madela Abrudan, University of Oradea, RomaniaMiriam Jankalová, University of Zilina, SlovakiaMiroslav Iordanov Mateev, American University, Dubai, UAEModar Abdullatif, Middle East University, Jordan,Mohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMongi Arfaoui, University of Monastir, TunisiaMonika Wieczorek, University of Economics in Katowice, PolandMuath Eleswed, American University of Kuwait, USAOzgur Demirtas, Turkish Air Force Academy, TurkeyPascal Stiefenhofer, University of Brighton, UKRadoslav Jankal, University of Zilina, SlovakiaSumathisri Bhoopalan, Sastra University, IndiaTamizhjyothi Kailasam, Annamalai University, IndiaValerija Botric, The Institute of Economics, Zagreb, CroatiaVassili JOANNIDES de LAUTOUR, Queensland University of Technology School of Accountancy, FranceWejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), Tunisia
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 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 | 0.039 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.114 | 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".