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Record W7030394965

Methodological specifics of forecasting the development of the industrial sector of a region’s economy factoring in the impact of shock “impulses” on it (Through the example of the republic of Tatarstan)

2015· other· en· W7030394965 on OpenAlexaboutno aff

Bibliographic record

Venuezvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus) · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFactoringInvestment (military)Shock (circulatory)LegislatureForeign direct investmentRelation (database)Relevance (law)Economic integrationRussian federationGlobalizationIndustrial relations
DOInot available

Abstract

fetched live from OpenAlex

© 2015 Canadian Center of Science and Education. All rights reserved. Present-day economic conditions are characterized by a high level of integration and interpenetration between national economic systems. This, in turn, determines the way economic relations that form as a result of the impact of not just internal but external “impulses” on them are to be coordinated and will be operating. The latter are formed, for instance, as a result of overcoming trade barriers in interstate relations or, on the contrary, as a result of restrictions imposed in relation to access to external resources and markets. There are two aspects that impart particular relevance to the processes of generating external impulses. Firstly, Russia’s entry into the World Trade Organization (WTO) in 2012 determined a whole spectrum of dimensions for the development and transformation of trade-economic processes (garnering better, compared with existing, and non-discriminatory conditions for the access of Russian products to foreign markets; creating a more favorable climate for foreign investment as a result of bringing the legislative system in line with WTO norms, etc.) (Gafurov, Safiullin, & Safiullin, 2012). Secondly, tensions building over the last several months between the Russian Federation and a particular segment of the global community are generating a number of serious risks associated with a set of institutional and market restrictions, which are directly or indirectly affecting the development of the industrial and financial sectors of the national and, consequently, regional economic systems.

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)0.032
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.007
Science and technology studies0.0000.022
Scholarly communication0.0000.001
Open science0.0120.002
Research integrity0.0010.002
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 head0.602
GPT teacher head0.427
Teacher spread0.175 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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