Exploring Factors Affecting the Sustainability of Islamic Affairs in Malaysia: An Interpretive Structural Modeling (ISM) Approach
Bibliographic record
Abstract
In Malaysia, Islamic affairs institutions play an essential role in ensuring the development and progress of Islamic affairs.However, in most cases, the institutions in charge of Islamic affairs face many challenges in maintaining the purity of the Islamic faith and teachings in this country.In response to this, it is imperative to understand the factors that can enhance the sustainability of Islamic affairs.Therefore, the objective of this paper is to explore the factors affecting the sustainability of Islamic affairs in Malaysia.By integrating Interpretive Structural Modeling (ISM) and Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC), this study developed a novel model to ensure the sustainability of Islamic affairs.The findings of the model indicated that Islamic Religious Education (IRE) is the most influential factor in creating the sustainability of Islamic affairs.On the other hand, Society Well-Being (SWB) is the most dependent factor influenced by all other factors considered in this study.Accordingly, it is postulated that the identification of significant factors and the utilization of the proposed model could guide various stakeholder groups, such as policymakers, policy executors, and Islamic affairs practitioners, to achieve long-term sustainability.This model could also be adapted by Islamic institutions in other countries to formulate appropriate efforts to strengthen the development of Islamic affairs based on the critical factors of relationships.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".