İSLÂM TARİHİ BİLİM DALINDA HAZIRLANAN LİSANSÜSTÜ TEZLER, KONULARI VE TEZ KONUSU BELİRLENMESİNDE KARŞILAŞILAN BAZI SORUNLAR
Bibliographic record
Abstract
Given the M.A. and Ph.D theses submitted to the Departments of Islamic History in 23 Theology Faculties in Turkey after the establisment of Higher Education Committee (YOK), it is seen that the majority of the theses were done in the 8 Universities in which there were either a Theology Faculty or an Institute of Higher Islamic Education before the YOK. More than 91 % of the theses until 2003 were done in these universities. This clearly indicates that Theology Faculties, especially those founded in the 90’s, are in need of lecturers who can supervise postgraduate theses. One quarter of the postgraduate theses on Islamic History in our country are Ph.Ds. and the rest are M.A. theses. Given the subject of these theses, one realizes that the majority of them are on issues related to the Ottoman period. One of the problems facing researchers is that several M.A. or Ph.D. candidates study the same subject at the same time. In my personal opinion, to avoid this problem, an internet site giving the latest information about ongoing theses on Islamic History urgently needs to be established.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".