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Record W2975033992 · doi:10.1080/00210862.2019.1646116

The Jewish Communities of Central Asia in the Medieval and Early Modern Periods

2019· article· en· W2975033992 on OpenAlexaff
Albert Kaganovitch

Bibliographic record

VenueIranian Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsJudaismAncient historyCentral asiaHistoryCircumstantial evidenceIslamPopulationEthnic groupChinaGeographyArchaeologyDemographyAnthropologySociology

Abstract

fetched live from OpenAlex

When the Jews first settled in Central Asia is uncertain, but circumstantial evidence clearly indicates that this happened at least two and a half thousand years ago. In the first millenniumAD, the Jews lived only in cities no farther than 750 km east of the Caspian sea (in the eighth–eleventh centuries the sea was called Khazarian). Only later did they migrate to the central part of the region, to cities like Samarkand and Bukhara. It is possible that Jews from Khazaria joined them, since they already had tight trade connections with Central Asia and China. There is no trace of evidence regarding the existence of Jews in the entirety of Central Asia in the early sixteenth century. At the very end of the sixteenth century Bukhara became the new ethnoreligious center of the Jews in that region. In the first half of the nineteenth century, thanks to European travelers visiting Central Asia at that time, the term “Bukharan Jews” was assigned to this sub-ethnic Jewish group. Drawing on a wide range of primary and secondary source materials, this article aims to prove that the presence of Jews in Central Asia was not continuous, and therefore the modern Bukharan Jews are not descendants of the first Jewish settlers there. It also attempts to determine where Central Asia’s first Jewish population disappeared to.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.305
Teacher spread0.260 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2019
Admission routes1
Has abstractyes

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