Reviewer Acknowledgements for International Business Research, Vol. 13, No. 8
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 contact us for the application form at: ibr@ccsenet.org Reviewers for Volume 13, Number 8 Anca Gabriela Turtureanu, “DANUBIUS” University Galati, Romania Andrea Carosi, University of Sassari, Italy Andrei Buiga, “ARTIFEX University of Bucharest, Romania Anna Maria Calce, University of Cassino and Southern Lazio, Italy Anna Paola Micheli, Univrtsity of Cassino and Southern Lazio, Italy Antonio Usai, University of Sassari, Italy Anuradha Iddagoda , University of Sri Jayewardenepura, Sri Lanka Ashford C Chea, Benedict College, USA Ayoub Taha Sidahmed, SIU, Sudan Benjamin James Inyang, University of Calabar, Nigeria Chokri Kooli, International Center for Basic Research applied, Paris, Canada Dionito F. Mangao, Cavite State University – Naic Campus, Philippines Duminda Kuruppuarachchi, University of Otago, New Zealand Farouq Altahtamouni, Imam AbdulRahman Bin Fisal University, Jordan Fawzieh Mohammed Masad, Jadara University, Jordan Federico de Andreis, "UNIVERSITY “GIUSTINO FORTUNATO”Benevento", Italy Filomena Izzo, University of Campania Luigi Vanvitelli, Italy Florin Ionita, The Bucharest Academy of Economic Studies, Romania Hanna Trojanowska, Warsaw University of Technology, Poland Hillary Odor, University of Benin, Nigeria L. Leo Franklin, Bharathidasn University, India Marco Valeri, Niccolò Cusano University, Italy Maria Teresa Bianchi, University of Rome “LA SAPIENZA”, Italy Maria-Madela Abrudan, University of ORADEA, Romania Maryam Ebrahimi, Azad University, Iran Michele Rubino, Università LUM Jean Monnet, Italy Mihaela Simionescu, Institute for Economic Forecasting of the Romanian Academy, Romania Mohsen Malekalketab Khiabani, University Technology Malaysia, Malaysia Mongi Arfaoui, University of Monastir, Tunisia Ouedraogo Sayouba, University Ouaga 2, Burkina Faso Pascal Stiefenhofer, University of Exeter, UK Rafiuddin Ahmed, James Cook University, Australia Roberto Campos da Rocha Miranda, Brazilian Chamber of Deputies, Brazil Rossana Piccolo, University of Campania "Luigi Vanvitelli", Italy Sachita Yadav, Manav Rachna University, Faridabad, India Sara Saggese, University of Naples Federico II, Italy Sumathisri Bhoopalan, SASTRA Deemed to be University, India Tatiana Marceda Bach, Centro Universitário Univel (UNIVEL), Brazil Yan 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 | 0.029 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.095 |
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".