Experience in uncertainty macroeconomic conditions and demographic factors as determinants of sharia stock portfolios in Indonesia
Bibliographic record
Abstract
The purpose of this research is to know the influence of Uncertainty Macroeconomic and demographic factors such as Population, Gross Domestic Product, Government Sukuk and Sharia Mutual Funds to The Investment Yield Sharia Insurance in Indonesia. The method of research using a causal research design in Indonesia Sharia Insurance. The data used are secondary data sourced from Financial Services Authority, Indonesia. The method of analysis used in this research is the SEM method using SmartPLS software. The results show that Population, Gross Domestic Product, Government Sukuk and Sharia Mutual Funds do not influence Investment Yield Sharia Insurance in Indonesia. The implications of this research are the Indonesian Financial Services Authority policy which considers Population, Gross Domestic Product, Government Sukuk and Sharia Mutual Funds for Investment Yield Sharia Insurance in Indonesia. The limitation of this research is to use monthly samples and only observe for 2 (two) years. Originality of this research is using the latest observation variable that is 2018-2019 and observation at a sharia insurance company in Indonesia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".