Reviewer Acknowledgements for International Business Research, Vol. 9, No. 12
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 9, Number 12Alina BadulescuAlireza AthariAmran AwangAnna Helena JankowiakAnna Paola MicheliAntonella PetrilloArash RiasiAshford C CheaAurelija BurinskieneBadar Nadeem AshrafBenjamin James InyangCelina Maria OlszakCheng JingCristian Marian BarbuFabio De FeliceFadi AlkaraanFrancesco CiampiGeorgeta DragomirGuillaume MarceauHanna TrojanowskaIonela-Corina ChersanIvo De LooIwona Gorzeń-MitkaJabbouri ImadJanusz WielkiManlio Del GiudiceMansour Esmaeil ZaeiManuela Rozalia GaborMarcelino José JorgeMaria do Céu Gaspar AlvesMaria J. Sanchez-BuenoMaria Teresa BianchiMaria-Madela AbrudanMichaela Maria Schaffhauser-LinzattiMihaela SimionescuMiriam JankalováModar AbdullatifMonika WieczorekMuath EleswedRadoslav JankalRoberto Campos da Rocha MirandaRoxanne Helm StevensSumathisri BhoopalanValeria StefanelliVincent GrèzesWejdene Yangui
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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 | 0.032 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.152 | 0.115 |
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".