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Record W3128389812 · doi:10.17516/1997-1370-0567

Karasubazar: Historical Topography of the City of the Crimean Khanate in the 16th-18th Centuries

2020· article· en· W3128389812 on OpenAlexaboutno aff
Bocharov Sergei G.

Bibliographic record

VenueJournal of Siberian Federal University Humanities & Social Sciences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)GeographyPopulationAntiqueArmenianPlan (archaeology)Settlement (finance)State (computer science)Ancient historyArchaeologyCapital cityCapital (architecture)Ancient cityHistoryDemographySociologyEconomic geography

Abstract

fetched live from OpenAlex

The article covers the main points of the town-planning history of Karasubazar, the city of the Crimean khanate, and, most importantly, offers a graphic reconstruction of its master plan for the last quarter of the 18th century, the final stage of the state’s existence. Reconstruction of the historical topography of the late medieval city was carried out for the first time on the basis of three types of sources – written, cartographic, and archaeological. All the basic elements of the city’s historical topography as well as the plan of quarterly residential development and a network of streets are reconstructed. Characteristic features of the location of the quarters inhabited by the Greek, Armenian and Jewish population among the main population of the Tatar inhabitants are revealed. City mosques, bathhouses, fountains supplying the citizens with water, hotels-caravanserais, shopping malls, and production workshops are localized. It is found out that Karasubazar was the second largest settlement in the state, its capital Bakhchisarai being the largest one. By the final stage of the Crimean khanate’s existence the area of the urban development of Karasubazar was 109.0 hectares

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.256
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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