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 3 Alejandra Moreno Alvarez, Universidad de Oviedo, Spain Ali S.M. Al-Issa, Sultan Qaboos University, Oman Ani Derderian, WSU, USA Anna Grana, University of Palermo, Italy Annalisa Pavan, University of Padova, ITALY Edwards, Beverly L, Fayetteville State University Department of Social Work, United States Eugenia Panitsides, University of Macedonia, Greece Florin Ionita, The Bucharest Academy of Economic Studies, Romania Gabriela Gruber, Lucian Blaga University of Sibiu, Romania Gülce Başer, Boğaziçi University, Tukey Hiranya Lahiri, M.U.C Women’s College, Burdwan, India Ifigeneia Vamvakidou, University of Western Macedonia, Greece Ioan-Gheorghe Rotaru, ‘Timotheus’ Brethren Theological Institute of Bucharest, Romania Johnnie Woodard, Independent Scholar, USA Karen Ferreira-Meyers, University of Swaziland, Swaziland Lena Arampatzidou, Aristotle University of Thessaloniki, Greece Maria Pescaru, University of Pitești, ROMANIA Meenal Tula, University of Hyderabad, India Pri Priyono, universities PGRI adi buana, Indonesia Ronald James Scott, Leading-Edge Research Institute, USA Sara Núñez Izquierdo, University of Salamanca, Spain Smita M. Patil, School of Gender and Development Studies, India Szabolcs Blazsek, Universidad Francisco Marroquin, Guatemala Tryfon Korontzis, Hellenic National School of Local Government , Greece 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 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 | 0.130 | 0.580 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.127 | 0.077 |
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".