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Record W4313787730 · doi:10.1353/tmr.2014.0003

The Role of British Military Experts in the Formation of the Qajar Troops in the First Quarter of the Xix Century

2014· article· en· W4313787730 on OpenAlexaboutno aff
Nigar Gozalova

Bibliographic record

Venue˜The œMaghreb review/Maghreb review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsThroneQuarter (Canadian coin)LegislatureMemoirHistoryAncient historySubject (documents)LawPoliticsPolitical scienceLibrary scienceArchaeology

Abstract

fetched live from OpenAlex

This research deals with the role of British military experts in shaping Qajar troops during the Russian-Iranian wars of 1804–1813 and 1826–1828. The article is based on a wide range both of published works and archival material. A greater portion of yet-unpublished materials dealing with the activities of British officers in Iran, as well as diplomatic documents and legislative acts is kept at RSHA1 and RSAN2. Besides, the author has relied on materials of the British Library, Asia, Pacific and Africa Collections, including archival materials of India Office Records (1858-1947).3 In addition to archival materials, the research has referred to existing sources, correspondence between diplomatic representatives of Great Britain and Russia, Qajar Iran, diaries, memoirs, travel notes of participants and contemporaries of the epoch in question. The above facts make it possible to examine the eastern policy of England, its relations with Qajar Iran, and specifically, questions arising from aid (military experts, uniform and weaponry) and funding for army reorganization; also, the attempts made in this directions by the heir to the throne, fiAbbas Mirza (1789-1833). The article does not pretend to provide a comprehensive analysis of this scantily explored subject, but aims to give a brief review of striking examples of British-Qajar interaction in the military sphere.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.579
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venue˜The œMaghreb review/Maghreb reviewSame topicIslamic Studies and HistoryFrench-language works237,207