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Record W3203669322 · doi:10.22495/rgcv11i3p2

Islamic financial institutions: Performance comparison with Canadian banks

2021· article· en· W3203669322 on OpenAlexaffabout
Raef Gouiaa, Pierre-Richard Tidiani Gaspard

Bibliographic record

VenueRisk Governance and Control Financial Markets & Institutions · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsGovernment of CanadaUniversité du Québec en Outaouais
Fundersnot available
KeywordsIslamData envelopment analysisGovernment (linguistics)BusinessIslamic financeIslamic bankingFinancial systemFinanceAccountingEconomics

Abstract

fetched live from OpenAlex

The Canadian financial market is considered to be very conservative and has been using the same practices for a long time. The economies of some countries such as England have adopted a strategy of including Islamic finance in their market and this has produced very satisfactory results. Considering that Islamic finance has been growing in recent years, this type of practice could be relevant to the Canadian market. The objective of this article is to analyze whether the performance of Islamic financial institutions is comparable to traditional banks. A comparison of the efficiency of conventional and Islamic banks will be important to determine because they do not operate in the same way and their primary source of income is different. The results revealed that Islamic banks tended to perform better than conventional banks. Performance ratios were in most cases higher for Islamic banks. This observation was confirmed with the use of the data envelopment analysis (DEA) model, which measures efficiency and effectiveness at the bank level. The results show that although some Islamic banks had significantly fewer assets than conventional banks, they were still able to use resources more efficiently. This confirmed that Islamic finance is an option for Canada and that with government support it will be possible to have a stronger economy overall.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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 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

Citations1
Published2021
Admission routes2
Has abstractyes

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