Reviewer Acknowledgements for International Business Research, Vol. 10, No. 5
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 5 Alireza Athari, Eastern Mediterranean University, IranAmaresh C. Das, Southern University at New Orleans, USAAmran Awang, Head of Entrepreneurship Center, MalaysiaArash Riasi, University of Delaware, USAAshford C Chea, Benedict College, USAAurelija Burinskiene, Vilnius Gediminas Technical University, LithuaniaBenjamin James Inyang, University of Calabar, NigeriaBrian Sheehan, Thaksin University, ThailandCelina Maria Olszak, University of Economics in Katowice, PolandCristian Marian Barbu, “ARTIFEX” University, RomaniaEva Mira Bolfíková, Univerzity of P. J. Šafárik in Košice, Slovak RepublicFlorin Ionita, The Bucharest Academy of Economic Studies, RomaniaFrancesco Ciampi, Florence University, ItalyFrancesco Scalera, University of Bari "Aldo Moro", ItalyGiuseppe Russo, University of Cassino and Southern Lazio, ItalyGuillaume Marceau, University of Aix-Marseille, FranceHanna Trojanowska, Warsaw University of Technology, PolandHuijian Dong, Pacific University, USAJolita Vveinhardt, Vytautas Magnus University, LithuaniaKherchi Ishak, University of Hassiba Ben Bouali De Chlef, AlgeriaM. Muzamil Naqshbandi, University of Dubai, UAEManlio Del Giudice, University of Rome "Link Campus", ItalyMansour Esmaeil Zaei, Panjab University, India/IranManuela Rozalia Gabor, “Petru Maior” University of Tîrgu Mureş, RomaniaMaria do Céu Gaspar Alves, University of Beira Interior, PortugalMaria Teresa Bianchi, UNIVERSITY OF ROME “LA SAPIENZA”, ItalyMaryam Ebrahimi, Azad University, IranMiroslav Iordanov Mateev, American University, Dubai, UAEMohamed Rochdi Keffala, University of Kairouan, TunisiaMohsen Malekalketab Khiabani, University Technology Malaysia, MalaysiaMuath Eleswed, American University of Kuwait, USARadoslav Jankal, University of Zilina, SlovakiaRiccardo Cimini, University of Tuscia, Viterbo, ItalyTamizhjyothi Kailasam, Annamalai University, IndiaValerija Botric, The Institute of Economics, Zagreb, CroatiaWejdene Yangui, Institute of High Business Studies of Sfax _ Tunisia (IHEC), TunisiaYan Lu, University of Central Florida, 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".