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Record W4361288794 · doi:10.52859/jbm.v11i2.313

Analisis Komparatif Bank Syariah Dan Bank Konvensional Pada Masa Resesi Global COVID-19

2023· article· en· W4361288794 on OpenAlexaboutno aff
Irvin Ng, Yulfiswandi Yulfiswandi, Junita Junita, Viviani Viviani, Lindawati Lindawati, Joey Joey

Bibliographic record

VenueJurnal Bina Manajemen · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsIslamRecessionFinancial systemCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)PopularityBusinessPopulationFinancial crisisIslamic bankingEconomicsDevelopment economicsGeographyPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

The popularity of islamic bank has been increasing on countries with Muslims as majority of their population where Muslim consumers could fulfil their banking needs while still complying to their religious rules. Development of islamic banks started from the establishment of Bank Muamalat as the first islamic bank established in Indonesia around 1991. Few studies has shown that islamic banks in Indonesia has better robustness than conventional banks during the 1998 economic crisis. In order to verify that statement, an updated study is required with the latest data and current economic situations. Indonesia has been impacted by the COVID-19 pandemic in which yearly GDP of Indonesia shown a negative growth as the recession status has been attained in the 2nd quarter of 2020. This study is done by comparing islamic banks as well as conventional banks’ fundamentals before and during the pandemic era. It is found that islamic banks is better in overcoming economic crisis than conventional banks. Keywords: Islamic banks, conventional banks, recession, COVID-19

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.071
GPT teacher head0.377
Teacher spread0.306 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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