Is Islamic Banking and Finance Doing Enough? Shaping the Sustainable and Socially Responsible Investment Community
Bibliographic record
Abstract
Islamic finance assets advanced at double-digit rates during the past decade, from about US$200 billion in 2003 to an estimated US$1.8 trillion at the end of 2013 (Ernst & Young 2014; IFSB 2014; Wyman 2009). Hence, despite this growth, Islamic finance and its related products are still focused in the Gulf Cooperation Council (GCC) countries, and Malaysia, and represent less than 1 percent of global financial assets. While, Islamic banking and finance sector, should responsive to small medium enterprises mitigating liability of smallness and newness. The factors mitigating the inherent liabilities associated with new entrepreneurial startups were found to be institutional support. Institutional support was also found to be an important factor of success for new startups. The primary focus of this study is to examine the critical role of Islamic Banking and Finance, expanding and facilitating entrepreneurial opportunities. This study draws on triple bottom line concept (people, planet and profit), by developing standards equivalent to triple bottom line reporting for Islamic banking and financial institutions, and disseminating independent and objective research to relevant stakeholders. This includes examining the potential positive or negative social impact of Islamic Banking and Finance on the financially sustainable and responsible community.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".