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Record W2602512052 · doi:10.5539/ibr.v10n4p139

The Role of the Jordanian Banking Sector in Economic Development

2017· article· en· W2602512052 on OpenAlexvenueno aff
Abedalfattah Zuhair Al-abedallat

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGross domestic productNull hypothesisBusinessPopulationFinancial systemProduct (mathematics)Economic sectorRetail bankingEconomicsEconomic growthEconomy

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the impact of the Jordanian banking sector on economic development that was measured by the Gross Domestic Product (GDP). It aims to identify the role of the Jordanian banking sector in the support of economic development through the study of the size of the credit facilities offered by banks.The study relied on descriptive and analytic method, as well as on field study. The population of this study represents the working banks in Jordan, which offers various banking services to the customers. The tool of the study include data of credit facilities, banking deposits for Jordanian banking sector, and gross domestic product that were collected from the annual financial status of Jordanian Central Bank for the period (2000- 2015).The study found that there is a significant statistical impact of the factors (the deposits of the banking sector, Credit facilities) on Gross Domestic Product (GDP). The study rejected the null hypothesis and accepted the alternative hypothesis for the two hypotheses. Also, the study recommended that the Jordanian banking sector should expand in the granting of credit facilities to all economic sectors.

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)0.002
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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