Bibliographic record
Abstract
The life insurance is considered as a means for investment as well as to overcome problems resulting from ageing and death of the householder.In many countries, insurance premiums create considerable financial resources which can be used for the development of the insurance industry or other parts and to provide people with much more services.Among the most important indices to assess the insurance industry performance, the amount of insurance premium and its growth could be stated to have the most importance from the aspect of the situation and extension of the insurance industry.The present research aims to assess key factors for demanding life insurance and investment on insurance premium.So, deducing effectual variables in this alternative could be effectual for designing a decision making model especially for manages, brokers and agencies of insurance companies.The questionnaire was designed in Likert scale and distributed among 235 residents of Tehran in Iran.Cronbach alpha is calculated as %86, which is well above the minimum desirable limit of 0.70.The study investigates 23 factors and extracts four important ones, which are econometric factors, the marketing performance factors, personal factors, and sociocultural factors.In this paper for analyzing the data, SPSS and Amos software's were used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.956 | 0.947 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".