Estate Planning Behaviour: A Systematic Literature Review
Bibliographic record
Abstract
Estate planning is a financial tool for an individual to manage wealth upon his incapacitation or death. Although there are numerous studies on estate planning, there still needs to be an effort to systematically review the estate planning measurements. The paper aims to systematically review the literature published between 1990 to 2021 and evaluate the measures of estate planning and its methodological qualities. The systematic literature review process was guided by the PRISMA protocol, where the articles were selected based on established databases such as WOS, SCOPUS, and Google Scholar. The final sample of 24 articles was reviewed for the estate planning measurement, and it was found that previous studies examined estate planning as a tool for financial, wealth distribution, and succession planning. The measurements of only 10 studies were found to be sufficiently validated, rendering other studies under review to be inadequate in establishing sound empirical conclusions. This review contributes to assisting future researchers in choosing well-validated measurements of estate planning and adapting them accordingly to each context.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".