Penentuan Besaran Premi Asuransi Jiwa Berjangka dengan Model True Fractional Premiums
Bibliographic record
Abstract
The model of the payment of life insurance premium that can be paid more than once a year is called fractional premiums. This model consists of two types, namely true fractional premiums and apportionable premium. The true fractional premiums is divided into two models of payment of compensation, namely discrete payment model and continuous payment model. This study aims to find out the comparison of 20 years life insurance premium with true fractional premiums model based on gender and number of payments made in a year from both payment models. The data used in this research is the simulation data. Based on the research result, it is found that the amount of life insurance premium using discrete compensation payment model is cheaper than the one using the continuous payment model. While based on gender, the premium of male is more expensive than female. Based on the amount of payments made in one year, payments made each month are more expensive than the payments made each quarter and semester.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".