232 Numaralı Defter Örneğinde Harp Tarihi Araştırmalarında Ordu Ruûs Defterlerinin Önemi Üzerine Bir Deneme (XVI. Yüzyıl)
Bibliographic record
Abstract
Although researches on political, economic and social history have been carried out mainly, military history, which is actually a very important value of nations, it draws attention in the academic community in recent years. Studies on the Turkish war history, which has found an important position today, mostly belong to after the XVIII. century. One of the reasons for this situation is undoubtedly access to resources. In addition to the records kept by the Ottoman Empire in the center and provinces, examining the records kept by the serdar during the military operation, will provide valuable information for history researches. Most of the appointments of state officials in the Ottoman geography were recorded in the ruûs registers. One of these registers is the army ruûs registers, which were kept under the supervision of the serdar while the army was on campaign. In this study, it will be tried to examine the ruûs records recorded during the Ottoman-Safavid wars that took place in the last quarter of the XVI. century. Also, the contribution of such registers to the understanding of warfare techniques, subsistence, supply, logistics, and military operations will be tried to be revealed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".