MétaCan
Menu
Back to cohort
Record W3115722415 · doi:10.18280/ijsdp.150807

Does the Commercial Bank's Loans Affect Economic Growth? Empirical Evidence for the Real Sector Economy in Kosovo (2005-2018)

2020· article· en· W3115722415 on OpenAlexvenueno aff
Fisnik Morina

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPrivate sectorContext (archaeology)Real economyEconomicsEconomic sectorOrder (exchange)Empirical researchEconomyBusinessFinancial systemEconomic policyFinanceEconomic growthMonetary economics

Abstract

fetched live from OpenAlex

The study aims to analyze the impact of credit policies of commercial banks on financing and development of the real sector of Kosovo's economy. In this context, some statistical and econometric models and techniques have been applied in order to test the impact of commercial banks through the lending process in the development of the real sector of Kosovo's economy for the period 2005-2018, using time series on a monthly basis. The empirical results of this study prove that commercial banks through the lending process have had a positive substantive impact on the development of the real sector in the economy of Kosovo. Economic development cannot happen without the development of the private sector and banks are the ones who can help and are helping in this regard. This study will provide a theoretical and practical analysis of contemporary forms of real sector lending, as well as, the importance of credit policy reform in financing and developing this sector and provide empirical evidence of how much bank loans have affected in the development of the real sector of Kosovo's economy.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.297
Teacher spread0.236 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicEconomic Growth and DevelopmentFrench-language works237,207