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 2 Alex Almici, Università degli Studi di Brescia, Italy Ana Souto, Nottingham Trent University, UK Anna Cebotari, Academy of Economic Studies of Moldova, Republica Moldova Bing Hiong Ngu, The University of New England, Australia Carmen Ramos, University of Oviedo, Spain Dave Williams, Dublin Institute of Technology, Ireland Edwards, Beverly L, Fayetteville State University Department of Social Work, United States Emilio Greco, "Sapienza" University of Rome, Italy Gabriela Gruber, Lucian Blaga University of Sibiu, Romania George Mathew Nalliveettil, Aljouf University, Saudi Arabia George Touche, Texas A&M University, USA Katja Eman, University of Maribor, Slovenia Lena Arampatzidou, Aristotle University Of Thessaloniki, Greece Maheran Zakaria, Universiti Teknologi MARA, Malaysia Maria Pescaru, University of Pitești, ROMANIA Meenal Tula, University of Hyderabad, India Nasina Md, Universiti Sains Malaysia, Malaysia Natalija Vrecer, Slovenian Institute for Adult Education (SIAE), Slovenia Nunzia Di Cristo Bertali, Liverpool John Moores University, United Kingdom Patrick van Esch, Moravian College, Australia & US Sara Núñez Izquierdo, University of Salamanca, Spain Savanam Chandra Sekhar, St. Ann’s College of Engineering & Technology, Chirala, India Skaidrė Žičkienė, Šiauliai University, Lithuania 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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 teacher head, 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".