Bibliographic record
Abstract
In this study, the administrative and demographic structure of the villages of Keban district in the first half of the 19th century was investigated by using the Ottoman population records as a source. Today, Keban is a district center connected to the province of Elazig. Keban was conquest by the Ottomans in 1515 and managed as a district (nahiya) subject to Cemisgezek for a long time. Having lead and silver reserves, Keban became increasingly important in the Ottoman Empire from the beginning of the 18th century when these mines began to be removed. In parallel with this increase in importance, Maadin-i Humayun Emaneti was established in 1775, and Keban town has increased to the central position of this administration. In addition, many kadas (districts) around Keban have been included in the Maadin-i Humayun Emaneti from financial and administrative perspectives. The villages of today's Keban district were administratively linked to Cemisgezek, Harput, Arapgir and Arguvan kadas. Although all of these villages were Muslim villages, there were very few non-Muslim populations in some of the villages. But in the second half of the 19th century, important changes were made to the administrative structure of the kadas that constituted Maadin-i Humayun. Especially in the last quarter of the century when mining activities were ended, Arguvan was incorporated into Keban, which had been turned into a nuke by narrowing the borders with the numerous villages of Arapgir, Cemisgezek and Harput.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".