Reviewer Acknowledgements for International Business Research, Vol. 10, No. 10
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 10Alina Badulescu, University of Oradea, RomaniaAshford C Chea, Benedict College, USAAtallah Ahmad Alhosban, Aqaba University of Technology, JordanAurelija Burinskiene, Vilnius Gediminas Technical University, LithuaniaBenjamin James Inyang, University of Calabar, NigeriaCelina Maria Olszak, University of Economics in Katowice, PolandDea’a Al-Deen Al-Sraheen, Al-Zaytoonah University of Jordan, JordanEjindu Iwelu MacDonald Morah, University of Westminster, London, UKEva Mira Bolfíková, Univerzity of P. J. Šafárik in Košice, Slovak RepublicFederica De Santis, University of Pisa , ItalyFlorin Ionita, The Bucharest Academy of Economic Studies, RomaniaFoued Hamouda, Ecole Supérieure de Commerce, TunisiaFrancesco Ciampi, Florence University, ItalyHanna Trojanowska, Warsaw University of Technology, PolandHerald Monis, Milagres College, IndiaHongliang Qiu, Tourism College of Zhejiang, ChinaHsiao-Ching Kuo, Washington and Jefferson College, USAHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaJoanna Katarzyna Blach, University of Economics in Katowice, PolandJorge Mongay-Hurtado, ESIC Business and Marketing School, SpainMansour Esmaeil Zaei, Panjab University, India/IranMarcelino José Jorge, Evandro Chagas Clinical Research Institute of Oswaldo Cruz Foundation, BrazilMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMithat Turhan, Mersin University, TurkeyMuath Eleswed, American University of Kuwait, USAPascal Stiefenhofer, University of Brighton, UKRadoslav Jankal, University of Zilina, SlovakiaRafiuddin Ahmed, James Cook University, AustraliaRoberto Campos da Rocha Miranda, University Center Iesb, BrazilRoxanne Helm Stevens, Azusa Pacific University, USASang-Bing Tsai, University of Electronic Science and Technology of China, ChinaSerhii Kozlovskiy, Donetsk National University, UkraineShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongSumathisri Bhoopalan, Sastra University, IndiaVassili JOANNIDES de LAUTOUR, Grenoble École de Management (France) and Queensland University of Technology School of Accountancy (Australia), FranceVincent Grèzes, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), SwitzerlandWejdene 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 | 0.045 | 0.364 |
| Meta-epidemiology (narrow) | 0.003 | 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) | 0.118 | 0.088 |
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".