Grandparents Raising Grandchildren and the Implications for Inheritance
Bibliographic record
Abstract
Today in the United States, thousands of grandparents are raising their young grandchildren because the children's parents are ill, disabled, imprisoned, or otherwise unable to care for them. This can create problems if the grandparents have not done any estate planning, since intestacy laws mandate that children, rather than grandchildren, receive a decedent's assets in intestacy. This article offers an analysis of the statistical data on how many grandparents are raising their grandchildren, which is part of a broader trend of children being raised by non-parental relatives, or kinship care. This data is further analyzed to determine the likelihood that these grandparents have estate plans. Then, the article discusses the posibility of expanding existing legal doctrines, including equitable adoption and pretermitted child statutes, to solve these types of problems. It also discusses the possibility of adopting a family maintenance system, already in place in New Zealand, Australia, England, and many Canadian provinces, in the United States. The article concludes by discussing what might be the best, and simplest, solution in this situation: having the grandparent either write a will or provide a gift under the Uniform Transfer to Minors Act (UTMA).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".