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Record W3027352653 · doi:10.36742/2410-0919-2020-1-8

INTERNATIONAL EXPERIENCE OF FUNCTIONING OF THE CREDIT SERVICES MARKET

2020· article· en· W3027352653 on OpenAlexaboutno aff
Артур Жаворонок

Bibliographic record

VenueThe economic discourse · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsLoanRestructuringBusinessEconomicsCredit historyBond marketCredit riskFinancial systemFinance

Abstract

fetched live from OpenAlex

Introduction. The country's economy is still in a state where its development is hampered by the absence of clearly defined, priority programs to stimulate business and economic sectors, including through credit. Bank lending to business entities is an effective way of getting out of the crisis. Strengthening the role of credit relations as a means of stimulating the development of production is manifested in various aspects. Methods. Fundamental economic theories, lending theories, financial studies, as well as research by scholars and foreign scientists are investigated in the research. They used the generally accepted principles of scientific research to make their decision. A number of general scientific methods were used in the process of research, in particular: analogies and logical generalization (to study the prospects of bank lending); systematic analysis (to study the organization of the process of bank lending in the market of credit services), etc. Results. Based on the isolated problems, the foreign experience of countries such as the USA, Germany, Argentina, Mexico, Poland, Canada and Italy was explored, on the basis of which the prospects for improvement of the bank lending mechanism in Ukraine and credit monitoring of the borrower in particular were outlined. Discussion. Given the overseas experience of developed countries, it is possible to distinguish: a combination of different methods of restructuring problem loans; when assessing the potential risk of default on a loan, use different methods of determining it ("SAMRARI", "PARSEL" or "Rule 5 C"); when making class calculations, make corrective adjustments to the credit score of the borrower. Prospects for further studies of the credit services market may be the intensification of bank lending, which certainly implies further liberalization of refinancing policy, taking into account foreign experience. Keywords: credit, credit relations, credit policy, credit services market, bank lending.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.002

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 head0.023
GPT teacher head0.241
Teacher spread0.217 · 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 designNot applicable
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

Citations5
Published2020
Admission routes1
Has abstractyes

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