Heritage of the Golden Horde: the origins of Crimean Khanat cities
Bibliographic record
Abstract
Golden Horde State during its existence on the Crimean Peninsula origin two towns Solkhat – Krym (modern Stariy Krym) and Kirk-Yer (modern Chufut-Kale). At the time of its emergence in the mid-15th century, the Crimean Khanate "inherited" only these two towns on the peninsula. Coastal Genoese towns - Caffa (modern Feodosia), Soldaia (modern Sudak), Cembalo (modern Balaklava) and Vosporo (modern Kerch) were situated near as well as two towns of the Late Byzantine principality Theodoro: the capital of the principality – Theodoro (now Mangup) and the town Calamita (now Inkerman). As a result of the Ottoman conquest of 1475 the number of Ottoman Crimean towns remained the same, only their names were changed: Caffa became Kefe, Soldaia – Sudak, Cembalo – Balaklava, Vosporo – Kerch, Theodoro – Mangup, Calamita – Inkerman. The total number of the Ottoman Crimean towns remained virtually unchanged for three centuries. In contrast, in the territory of the Crimean Khanate in the last quarter of the 15th and early 16th centuries five new towns were founded. Bahchisaray, Karasubazar, Ak-Mechet, Gezlev and Or Kapu were added to two old Golden Horde cities – Solkhat and Kirk-Yer. It were new towns that got priority in development. The political and economic center of Golden Horde Solkhat in the second half of the 15th century would lose its administrative importance and economic influence. During the khan's period it would be called Eski Krym. The main conclusion of the study is that all new towns of the Crimean Khanate (Bahchisaray, Karasubazar, Ak-Mechet, Gezlew, Or Kapu) were not connected with the previous centuries-old urbanistic tradition of local Byzantine or Genoese cities, they appeared in previously unoccupied places, where at best there were Golden Horde settlements. The original urban planning foundations of these cities come from the Golden Horde (in the broad sense – the Eastern) urban planning tradition.
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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 | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".