Bibliographic record
Abstract
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 11, Number 1 Alejandra Moreno Alvarez, Universidad de Oviedo, Spain Arthur Becker-Weidman, Center For Family Development, USA Carmen Ramos, University of Oviedo, Spain Dave Williams, Dublin Institute of Technology, Ireland Efstathios Stefos, University of the Aegean, Greece Emilia Salvanou, Hellenic Open University, Greece Gabriela Gruber, Lucian Blaga University of Sibiu, Romania George Touche, Texas A&M University, USA Hyunsook Kang, Stephen F. Austin State University, United States Ifigeneia Vamvakidou, University of Western Macedonia, Greece Ioan-Gheorghe Rotaru, ‘Timotheus’ Brethren Theological Institute of Bucharest, Romania Ioanna Efstathiou, University of the Aegean, Greece Karen Ferreira-Meyers, University of Swaziland, Swaziland Macleans Mzumara, Bindura University of Science Education, Zimbabwe Maria-Eleni Syrmali, Panteion University, Greece Meenal Tula, University of Hyderabad, India Mehdi Ghasemi, University of Turku, Finland Mirosław Kowalski, University of Zielona Góra, Poland Nikos Christofis, Shaanxi Normal University, Greece Rebecca Burwell, Westfield State University, USA Rickey Ray, Northeast State Community College, USA Ronald James Scott, Leading-Edge Research Institute, USA Savanam Chandra Sekhar, St. Ann’s College of Engineering & Technology, Chirala, India Serena Kelly, University of Canterbury, New Zealand Smita M. Patil, School of Gender and Development Studies, India Szabolcs Blazsek, Universidad Francisco Marroquin, Guatemala
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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 | 0.167 | 0.676 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.099 | 0.054 |
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".