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Record W2724130573 · doi:10.1111/russ.12150

Currency Crises in Post‐Soviet Russia

2017· article· en· W2724130573 on OpenAlexaff
Juliet Johnson, David M. Woodruff

Bibliographic record

VenueThe Russian Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsMcGill University
Fundersnot available
KeywordsDepreciation (economics)CurrencyGovernment (linguistics)PoliticsEconomicsEconomic policyRevenueCapital (architecture)Political scienceEconomic systemPolitical economyDevelopment economicsInternational economicsEconomyMarket economyMonetary economicsFinancial capitalFinanceCapital formationGeographyHuman capital

Abstract

fetched live from OpenAlex

Currency crises have been a recurrent feature of Russia's post‐Soviet experience. This article examines three episodes of sudden and sharp ruble depreciation in 1998, 2008, and 2014–16. We examine these crises and their political consequences as iterative episodes in the Russian government's ongoing efforts to deal with its structural dependence on energy revenues and international capital flows. We argue that the 1998 crisis and the Russian government's response to it proved effective in transforming policies and institutions that had contributed to the ruble's collapse, but also paradoxically reinforced the central role of resource revenues and international capital flows in Russia's political economy. Policy decisions after 2008 then represented variations on a theme, leaving the Russian government better able to manage future currency crises while simultaneously maintaining and deepening the state's underlying structural vulnerabilities as well as its patronage‐based political‐economic system. The crisis of 2014–16 may, however, ultimately bring greater shifts in policy as Russia adapts to fundamentally changed international circumstances.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.411
Teacher spread0.348 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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