Green and Sustainable Life Insurance: A Bibliometric Review
Bibliographic record
Abstract
Presently, there is a growing concern about implementing sustainable practices among businesses worldwide. Risk management is observed to contribute to the promotion of exercised business sustainability significantly. The study aims to examine published articles focusing on the role of risk management in promoting business sustainability practices and its advancement in the Cambridge online database to determine the current trend direction of this field. The paper’s conducted analysis is based on bibliographic co-word clustering analysis of the collected studies from the database. The research’s output disclosed four keyword clusters in the gathered articles’ titles and identified the most interested journals, countries, authors, subject areas, and organizations in the said topic and its popular research period. Based on the research output, recommendations regarding future research were provided, including expanding the list of databases for the data collection phase and utilizing the bibliographic coupling relations approach in the bibliometric analysis.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Bibliometric co-word analysis of the published literature on risk management and sustainability in insurance; bibliometric method whose object is a research literature and its trends.
It uses bibliometric analysis to study a body of published research, making it adjacent metaresearch.
Bibliometric review maps a research literature on green life insurance; object is publication patterns, borderline domain map.
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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.138 | 0.154 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".