The nature of Islamic socio‐scientific inquiry Theory and application to capital markets
Bibliographic record
Abstract
A theoretical methodology premised on the epistemology of divine unity as the world view of all Islamic socio‐scientific inquiry, is introduced. This general theory based on the knowledge‐centred interactive, integrative and evolutionary process of social becoming is next formulated for the specific case of capital markets. The methodology premised on unity and unification of knowledge is shown to be a universal application interconnecting science and society through a process‐oriented knowledge‐centred model. Finally, the generalized theoretical methodology and its specification to the case of Islamic capital market is used to critically evaluate Malaysia’s Islamization program, specially during the heady days of her stock market and currency turmoils. Alternative policy recommendations are provided for a newer outlook on Malaysian development and capital market Islamization programs. A general inference is thus derived and conveyed to the field of capital market stability arising from a direct linkage between real sectoral activities and endogenous money as store of value of real transactions. The approach of this paper being epistemological in nature, it undertakes a fundamental look at Qur’an and Sunnah for developing shari’ah‐rules (Islamic law), i.e. ahkam as‐shari’ah (rules derived from shari’ah), in the area of socio‐scientific inquiry in general and Islamic capital market in particular.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".