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ECONOMIC SANCTIONS AND THE PROBLEM OF IMPORT SUBSTITUTION

2022· article· en· W4403215594 on OpenAlexaboutno aff
Liliya Abdrakhmanova, Elena Sergeevna Schigortsova

Bibliographic record

VenueScientific Review Theory and Practice · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsnot available
Fundersnot available
KeywordsSubstitution (logic)SanctionsEconomic sanctionsEconomicsInternational economicsBusinessInternational tradePolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

The article is devoted to the analysis of the sanctions imposed against Russia over the past decade and the problem of import substitution as a tool to increase the economic security of the state. After the announcement of the RF recognition of the independence of the Donetsk and Lugansk People's Republics (DPR and LPR) on February 22, 2022, and the subsequent special military operation of Russia to denazify and demilitarize Ukraine, a new stage of international relations began, associated with the introduction of an unprecedented scale (and constantly replenishing) sanctions on Russia by the United States, the European Union and some other states. The largest trade sanctions in history were imposed - a ban on the export of high-tech components and parts to the Russian Federation, as well as services and support related to this activity. Many trade agreements were annulled, and Russian banks were disconnected from the SWIFT interbank payment system. The foreign property and assets of many Russians have been seized. Flights are prohibited in the airspace of the European Union, Great Britain, Canada and other countries. The supply of Russian oil to America and Great Britain is limited, all foreign investments in the Russian economy under their jurisdiction are blocked. Foreign assets of the Central Bank of the Russian Federation are frozen. There are bans and restrictions in the field of broadcasting Russian media, social networks in the country. There is a break in diplomatic ties with a number of states. Many foreign companies have suspended their activities in the Russian market. Analysts predict the following main risks for Russian business: logistics crisis; restructuring trade routes; difficulties in meeting financial obligations and using financial instruments (leasing, factoring, etc.); limitation of the supply area; reduction in investment; reduced availability of loans; restriction of currency settlements; reputational costs, etc. In this regard, import substitution is one of the main directions of the strategy for developing the Russian economy and ensuring its security.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.014
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.032
GPT teacher head0.278
Teacher spread0.245 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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