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Record W2030366221 · doi:10.3167/sib.2013.120301

Great Expectations: Revisiting Canadian Economic Footprints in Siberia, 1890s–1921

2013· article· en· W2030366221 on OpenAlexaboutno aff
Jerry Black

Bibliographic record

VenueSibirica · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyArchaeologyEconomic geographyRegional sciencePolitical science

Abstract

fetched live from OpenAlex

Canada's interest in Russia's Far East and Siberia has a long history, propelled in the nineteenth century by London's Hudson's Bay Company driving eastward and St. Petersburg's Russian-American Company driving westward. Competition and sometime cooperation led to mutually beneficial projects shaping up in the early 20th century, among them plans to link up the Canadian Pacific Rail and Steamship Line with the Trans-Siberian in a trading complex that would have circumnavigated the world. The Great War, the Russian Revolution, and Civil War, sealed the fate of this grandiose vision. Studies on Western involvement in the Russian Civil War highlight, reasonably, the military dimensions of intervention. Canada sent troops to Siberia as well, but Ottawa's ambition was primarily trade. Using untapped Russian archival material and contemporary Siberian newspaper reports, this article revisits Canada's participation in Russia's postwar conflagration with emphasis on the extent to which expectation of economic gain shaped Canada's official and private presence in Siberia.

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)0.003
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 score0.135
Threshold uncertainty score0.977

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.019
Science and technology studies0.0190.006
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.239
Teacher spread0.228 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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