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Record W4249888852 · doi:10.5539/ibr.v10n11p219

Reviewer Acknowledgements for International Business Research, Vol. 10, No. 11

2017· article· en· W4249888852 on OpenAlexvenueno aff
Kevin Duran

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceStrategic studiesManagementPolitical scienceSociologyEconomicsComputer science

Abstract

fetched live from OpenAlex

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 11Antônio André Cunha Callado, Universidade Federal Rural de Pernmabuco, BrazilArash Riasi, University of Delaware, USACelina Maria Olszak, University of Economics in Katowice, PolandChuan Huat Ong, KDU Penang University College, MalaysiaDea’a Al-Deen Al-Sraheen, Al-Zaytoonah University of Jordan, JordanEva Mira Bolfíková, Univerzity of P. J. Šafárik in Košice, Slovak RepublicFederica Caboni, University of Cagliari, ItalyGiuseppe Granata, University of Cassino and Southen Lazio, ItalyHongliang Qiu, Tourism College of Zhejiang, ChinaHsiao-Ching Kuo, Washington and Jefferson College, USAHung-Che Wu, Nanfang College of Sun Yat-sen University, ChinaKaren Gulliver, Argosy University, Twin Cities, USAMaria Teresa Bianchi, University of Rome “LA SAPIENZA”, ItalyMiriam Jankalová, University of Zilina, SlovakiaMohamed Abdel Rahman Salih, Taibah University, Saudi ArabiaMohamed Rochdi Keffala, University of Kairouan, TunisiaMongi Arfaoui, University of Monastir, TunisiaMonika Wieczorek, University of Economics in Katowice, PolandMuath Eleswed, American University of Kuwait, USAOnur Köprülü, Mersin University, TurkeyRadoslav Jankal, University of Zilina, SlovakiaRoxanne Helm Stevens, Azusa Pacific University, USASerhii Kozlovskiy, Donetsk National University, UkraineShun Mun Helen Wong, The Hong Kong Polytechnic University, Hong KongSumathisri Bhoopalan, Sastra University, IndiaValeria Stefanelli, Università del Salento, ItalyVincent Grèzes, University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis), SwitzerlandYan 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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.106
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.340
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.106
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0080.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.007

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.

Opus teacher head0.144
GPT teacher head0.420
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2017
Admission routes1
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