A survey of the methods, aspects and trends of life insurance efficiency papers
Bibliographic record
Abstract
This paper explores studies that determine life insurance efficiency, an area that is gaining in recognition as being important to investigate. As well a scrutinization and exploration of the numbers and recent trends of methods and some aspects of life firm efficiency measurement is implemented. In its overview of life company efficiency items written since 1982 this project shows how the most fundamental elements of life enterprise efficiency estimation, for example the set-up and form of outputs and inputs, have been coped with. Therefore, an assessment of the overall results of efficiency studies is possible. Similarly, ideas for potential further research are portrayed. One conclusion drawn from the results of this project is that the steady increase in both the volume and scope of pieces scrutinizing life firm efficiency means that it is being perceived as being of greater importance. Consequently, this review will be of value to both practitioners and regulators concerned with this subject in that it will enable an assessment of which aspects of this field of study need more research and which are otherwise worth developing.
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 | 0.034 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.048 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".