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Analysis of Foreign Experience in the Financial Regulation of the Arctic Territories Development and its Application in the Northern Regions of the Russian Federation

2021· article· en· W3203260269 on OpenAlexaboutno aff
Roman V. Badylevich

Bibliographic record

VenueArctic and North · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticRevenueBusinessFinancial instrumentFinanceInvestment (military)Political sciencePoliticsEcology

Abstract

fetched live from OpenAlex

The article examines foreign experience in implementing regional financial policy in relation to the Arctic territories. It assesses the experience of such sub-arctic countries as Canada, Finland, Denmark, Norway, Sweden, and the USA. The paper identifies two groups of financial instruments of territorial devel-opment: within the framework of general regional policy (instruments of fiscal capacity equalization, taxa-tion instruments, instruments to increase investment attractiveness) and within the framework of special policy for the development of Arctic territories (program-targeted instruments, special development funds, direct allocation of funds for current expenses and development). It is concluded that the Arctic countries apply different approaches and tools to the development of the regions located in the Arctic zone, the choice of which is determined by the type of state structure, the degree of financial independence of the regions in the sphere of financial regulation, the level of development of the northernmost subjects compared to the rest of the country. In the conditions of Russia, it is possible to use the best foreign experience in the sphere of financial regulation of development of the regions located in the Arctic zone. In particular, it is possible to use the experience of applying program-targeted development tools, the formation of special development funds, which are based on revenues from the use of natural resources of the Arctic, as well as the experience of creating favourable conditions to attract investors for the implementation of economically attractive projects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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