A Tale of Two Pillars: Emergent Geographies of Islamic Finance in Bahrain and Kuala Lumpur
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".