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Record W4387412071 · doi:10.1017/s0968565023000057

Political violence and financial markets in Tsarist Russia

2023· article· en· W4387412071 on OpenAlexfundno aff
Christopher A. Hartwell

Bibliographic record

VenueFinancial History Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsnot available
FundersQueen's UniversityUniversity of CambridgeMcGill UniversityHarvard University
KeywordsPoliticsAuthoritarianismFinancial marketEquity (law)Capital marketPolitical violencePolitical economyEconomicsPolitical scienceFinancial systemDemocracyFinanceLaw

Abstract

fetched live from OpenAlex

There is little research studying the effects of political violence on financial markets over decades, especially in an atmosphere where the violence manifested itself in heterogeneous and geographically widespread ways. This article examines the authoritarian edifice of Tsarist Russia in the nineteenth century to examine the way in which capital markets perceived political instability in a country which had paradoxically strong financial institutions but weak political ones. Using a novel database on political violence in Russia in the nineteenth century matched to monthly financial data from Russian equity markets, this article provides strong evidence that Russia's financial markets were negatively affected in the long run by political violence. Consistent with modern views of financial information, the effects of political violence were quickly incorporated into asset prices, but the specific magnitude of such violence was different depending on where the violence occurred and in what manner. Overall, it appeared that political violence was perceived very negatively by investors in Russian equity markets.

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)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.371
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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