Bibliographic record
Abstract
The aim of this paper was to investigate the effect of the gross domestic product (GDP) and population on the investment yield of sharia insurance in Indonesia. This research used a causal research design with Indonesian sharia insurance as the focus. The secondary data were sourced from the Financial Services Authority (OJK) of Indonesia in 2016–2017. The analysis was performed with Smart Partial Least Square (PLS) software and indicated that the GDP did not influence the investment yield; however, the population did influence the investment yield of sharia insurance in Indonesia. The implications of this study are expected to recommend to the Indonesia Financial Services Authority regarding the impact of the GDP and population on the investment yield in Indonesia. In addition, the implication provides support for the Indonesian monetary policy authorities to anticipate the monetary policy by the Fed, regarding dovish and hawkish sentiments, to encourage capital inflows to emerging countries due to the impact on the development of Sharia/Takaful insurance in Indonesia. A social implication is that the sharia insurance industry in Indonesia can develop if the public can enjoy convenience in applying for premiums and ease in receiving sharia insurance claims. The majority of Indonesia's population of Moslems requires an openness in the process. This study takes a sample of different sharia industry characteristics to compare sharia and conventional types of industry.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".