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Record W2900569336 · doi:10.6000/1929-7092.2018.07.43

The Main Directions of the Bank of Russia’s Activity in the System of Integrated Financial Regulation

2018· article· en· W2900569336 on OpenAlexvenueno aff
Irina E. Shaker, N. S. Shaker

Bibliographic record

VenueJournal of Reviews on Global Economics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial regulationSystemic riskArbitrageMoral hazardBusinessStandardizationFinancial systemFinanceFinancial servicesFinancial riskOrder (exchange)EconomicsAccountingFinancial crisisMarket economyIncentive

Abstract

fetched live from OpenAlex

Actuality : The transition to integrated financial regulation brought the issue on the central banks’ role in the new configuration of financial supervision; on the necessity of resistance to financial crises and systemic risks; on overcoming of sectoral fragmentation in the area of regulation and supervisory activities’ standardization based on best supervisory practices. Novelty : The regulatory and supervisory function has evolved into a critical factor in management of the financial system stability. Rethinking of the essence of the financial integral regulation has allowed distinguishing three separate directions: ensuring of financial stability, macroprudential oversight and regulation of moral hazard risk in the banking sector, financial institutions’ business behavior and limitation of the intention to establish the regulatory arbitrage. Practical Value : In countries, that have adopted the integral regulation of the financial market, in relation to all its sectors, the authors propose to develop and apply standards of the moral hazard risk regulation in the banking sphere and the business behavior of financial institutions; universal standards and technologies of regulation and supervision in order to limit the establishment of regulatory arbitrage; leveling of systemic risks of financially-credit sphere.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 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
Published2018
Admission routes1
Has abstractyes

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