Bibliographic record
Abstract
Dr. Hasan Sungur CIVELEK Dr. Ilhan TARIMER Dr. Arif NESRULLAZADE Dr. Ahmet BALCI Dr. Fikret KACAROĞLU Dr. Ergun BAYSAL Dr. Osman Zeki HEKIMOĞLU Dr. Zeynep Fidan KOCAK Dr. Tayfun BUKE Dr. Ali KECEBAS Dr. Dursun AYDIN Dr. Deniz ULGEN Dr. Soner TASLAK Dr. Mehmet UĞURLU Dr. Latif TASKAYA Dr. Baris Ethem SUZEK Dr. Desire RASOLOMAMPIONONA Politechnika Warszawska, POLAND Dr. Inan GULER 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 YIĞIT, Suleyman Demirel University, TURKEY Dr. Yavuz CAKIR, 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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".