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Record W7128502261 · doi:10.64903/1480-6800.23.2.89

A Tale of Two Pillars: Emergent Geographies of Islamic Finance in Bahrain and Kuala Lumpur

2020· article· W7128502261 on OpenAlexvenueno aff
Ryan Dicce, Michael Ewers, Jesse P.H. Poon, Yew Wah Chow

Bibliographic record

VenueArab world geographer · 2020
Typearticle
Language
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamIslamic financeEphemeral keyKuala lumpurFinancial servicesFinancial crisisGeography of finance

Abstract

fetched live from OpenAlex

Although primarily concentrated in Muslim counties, Islamic banking and finance (IBF) has become a global industry representing both a decentering of the global financial architecture and the emergence of an alternative global-urban network. While geographers have identified the “Mecca’s” of IBF, there remains a need to investigate the constituent elements and processes that define Islamic finance and determine its spatial manifestation organization, as well as the factors which differentiate its global-urban landscape from conventional financial centers. This includes traditional locational factors – the presence of IBF firms and financial professionals, consumer markets, and regulators – but also more dynamic and ephemeral elements, including the significance of global shari’a scholar networks, the nature of secondary bond (sukuk) markets, and the role of governing bodies. Therefore, we examine the urban manifestation of the global IBF industry through a comparative study of leading centers in Manama, Bahrain, and Kuala Lumpur, Malaysia. By presenting results from a survey of financial firms and key-informant interviews with industry actors, it assesses both cities as separate entities to determine geographic variation between centers, while also identifying the similarities between the two, uncovering the essential elements of Islamic financial centers and the various pathways to creation and niches they occupy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.010
GPT teacher head0.219
Teacher spread0.210 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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