Reviewer Acknowledgements for International Business Research, Vol. 10, No. 1
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 1 Alireza AthariArash RiasiAshford C CheaAurelija BurinskieneBenjamin James InyangEva Mira BolfíkováFabrizio RossiFadi AlkaraanFevzi EsenFlorin IonitaFrancesco CiampiFrancesco ScaleraGeorgeta DragomirGiuseppe GranataHanna TrojanowskaIonela-Corina ChersanKaren GulliverL. Leo FranklinM. Muzamil NaqshbandiManuela Rozalia GaborMarcelino José JorgeMaria João GuedesMiriam JankalováMohsen Malekalketab KhiabaniMongi ArfaouiMonika WieczorekMuath EleswedOzgur DemirtasPriyono Pri PriyonoRadoslav JankalRafiuddin AhmedRoberto Campos da Rocha MirandaSang-Bing TsaiSumathisri BhoopalanTamizhjyothi KailasamTerrill FrantzValeria StefanelliValerija BotricVassili JOANNIDES de LAUTOUR
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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 | 0.039 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.138 | 0.101 |
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".