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Record W4246075243 · doi:10.5539/res.v12n1p106

Reviewer Acknowledgements for Review of European Studies, Vol. 12, No. 1

2020· article· en· W4246075243 on OpenAlexvenueno aff
Paige Dou

Bibliographic record

VenueReview of European Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCzechLibrary scienceHumanitiesClassicsArt historyPolitical scienceSociologyArtPhilosophyComputer science

Abstract

fetched live from OpenAlex

Review of European Studies 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. Review of European Studies 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 res@ccsenet.org. Reviewers for Volume 12, Number 1 Alejandra Moreno Alvarez, Universidad de Oviedo, Spain Antonio Messeni Petruzzelli, Politecnico di Bari, Italy Arthur Becker-Weidman, Center For Family Development, USA Aziollah Arbabisarjou, Zahedan University of Medical Sciences, Iran Eugenia Panitsides, University of Macedonia, Greece Federico De Andreis, University Giustino Fortunato, Italy Florin Ionita, The Bucharest Academy of Economic Studies, Romania Frantisek Svoboda, Masaryk University, Czech republic Gabriela Gruber, Lucian Blaga University of Sibiu, Romania Gevisa La Rocca, University of Enna “Kore”, Italy Ghaiath M. A. Hussein, University of Birmingham, UK Gülce Başer, Boğaziçi University, Tukey Ifigeneia Vamvakidou, University of Western Macedonia, Greece Indrajit Goswami, N. L. Dalmia Institute of Management Studies and Research, India Ioan-Gheorghe Rotaru, ‘Timotheus’ Brethren Theological Institute of Bucharest, Romania Julia Stefanova, Economic Research Institute – The Bulgarian Academy of Sciences, Bulgaria Karen Ferreira-Meyers, University of Swaziland, Swaziland Maria Pescaru, University of Pitești, ROMANIA Montserrat Crespi Vallbona, University of Barcelona, Spain Muhammad Saud, Universitas Airlangga, Indonesia Natalija Vrecer, independent researcher, Slovenia Nunzia Di Cristo Bertali, Liverpool John Moores University, United Kingdom Serdar Yilmaz, World Bank, USA Skaidrė Žičkienė, Šiauliai University, Lithuania Szabolcs Blazsek, Universidad Francisco Marroquin, Guatemala Tryfon Korontzis, Hellenic National School of Local Government, Greece Valeria Vannoni, University of Perugia, Italy Vicenta Gisbert, Universidad de La Laguna, Spain

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 imitation

Not 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.

metaresearch head score (Codex)0.169
metaresearch head score (Gemma)0.670
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.169
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1690.670
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0250.016
Science and technology studies0.0060.005
Scholarly communication0.0180.014
Open science0.0080.007
Research integrity0.0140.012
Insufficient payload (model declined to judge)0.1070.060

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 head0.162
GPT teacher head0.421
Teacher spread0.259 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations0
Published2020
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