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Record W4416369009 · doi:10.51255/2311-603x_2024_3_40

External loans of Russia before and after the Crimean War

2024· article· W4416369009 on OpenAlexaboutno aff
Sergey Lebedev

Bibliographic record

VenueПетербургский исторический журнал · 2024
Typearticle
Language
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsRothschildState (computer science)Competitor analysisCommissionGovernment (linguistics)EmpireMercantilismQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Against the backdrop of a history of the rise and fall of international money markets, the article demonstrates the interaction of the Russian Empire with evolving intermediaries for the sale of government loans. This article represents the inaugural utilisation of business letters to the Parisian Rothschild of the tra- ding house of L. Stieglitz. The latter concentrated its activities not only on commission transactions with loans as a court banker during the initial six decades of the nineteenth century, but also on significantly expanded bill transactions, export maritime trade with European countries and, in particular, copper. The expansion of con- tinental joint-stock commercial banks positioned them as competitors of the international Rothschild group in the field of Russian state credit until the end of the Russian Empire. The author concentrated on the initial stages of this process, which occurred during the second quarter of the nineteenth century.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.280
Teacher spread0.270 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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