Bibliographic record
Abstract
Dr. Hasan Sungur CİVELEK Dr. İlhan TARIMER Dr. Arif NESRULLAZADE Dr. Ahmet BALCI Dr. Fikret KAÇAROĞLU Dr. Ergün BAYSAL Dr. Osman Zeki HEKİMOĞLU Dr. Zeynep Fidan KOÇAK Dr. Tayfun BÜKE Dr. Ali KEÇEBAŞ Dr. Dursun AYDIN Dr. Deniz ÜLGEN Dr. Soner TASLAK Dr. Mehmet UĞURLU Dr. Latif TAŞKAYA Dr. Barış Ethem SÜZEK Dr. Desire RASOLOMAMPIONONA Politechnika Warszawska, POLAND Dr. İnan GÜLER Gazi University, TURKEY Dr. John Mark DEAN University of South Carolina Columbia, USA Dr. Lech GRZESIAK Politechnika Warszawska, POLAND Dr. Cathy H. WU, University of Delaware, USA Dr. Matthew O.T. COLE, University of Bath, UK Dr. Nurettin KAYMAKCI, Middle East Technical University, TURKEY Dr. Richard TOSDAL, University of British Columbia, CANADA Dr. Daniela GIANNETTO, Mugla Sitki Kocman University, TURKEY Dr. Syed Ejaz AHMED, Brock University, CANADA Dr. Tuncay YİĞİT, Süleyman Demirel University, TURKEY Dr. Yavuz ÇAKIR, Embry – Riddle Aeronautical University, USA Dr. William SINGHOSE, Georgia Institute of Technology, 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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.657 | 0.554 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".