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Record W6981000408

Deserts, Natives, and Sylvan Cities: Russian Turkestan in American Travel Writing, 1890s – 1910s

2025· article· en· W6981000408 on OpenAlexaboutno aff

Bibliographic record

VenueRepository of the Academy's Library (Library of the Hungarian Academy of Sciences) · 2025
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsPoliticsCONQUESTCentral asiaChinaQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

The Russian conquest of Turkestan (the territories of modern Turkmenistan, Uzbekistan, Tajikistan, most of Kirghizstan, as well as the southern and southeastern areas of Kazakhstan), completed within three decades from the mid-1860s, brought controversial changes in the region’s economy, administration, and native lifestyle. The gradual emergence of railways and the development of agriculture (primarily cotton growing) attracted American entrepreneurs and travelers. This article covers two travel accounts in book form – parts of Siberia and Central Asia (1899) by Ohio businessman and philanthropist John Bookwalter (1839–1915) and Turkestan: “The Heart of Asia” (1911), written by journalist, travel writer, and diplomat William Curtis (1850–1911). I use the term “imperial view” to argue that the two Americans, despite their different social background and almost a decade passed between their travels, wrote about Russian Turkestan in a similar manner. They hoped to see the transformation of the “empty” nature of Turkestan for economic development, praised new Russian cities compared to native settlements, and created the image of Russia performing a “civilizing” mission in the region while lauding American economic presence there. However, they wrote differently about Central Asia as a geopolitical region due to the different life experiences of the authors and changes in the stance of both Russia and the U.S. in world politics in the 1900s.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.009
Scholarly communication0.0000.002
Open science0.0030.001
Research integrity0.0000.002
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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRepository of the Academy's Library (Library of the Hungarian Academy of Sciences)Same topicInfrared Thermography in MedicineFrench-language works237,207