Bibliographic record
Abstract
By analyzing a Tennessee bigamy case, a New York same-sex marriage case, and the growing cultural trend toward cohabitation over marriage, this article discusses how and why marriage is the best estate plan to protect vulnerable parties as they age. The article examines how marriage assists vulnerable parties in avoiding potential conflicts in estate planning and distribution, particularly when those parties have entered into alternative relationships. By focusing on the cases of Witherspoon, in which John Witherspoon entered into a bigamous second marriage, and Windsor, in which Edie Windsor is suing the U.S. government over the lack of federal tax recognition afforded her Canadian same-sex marriage, this article reveals how marriage expansion does not necessarily incentivize marriage, nor does it provide the benefits and protections often sought by those who enter into those marriage-like relationships. By contrasting the protection marriage affords to a vulnerable party in estate distribution and the dilemmas presented by marriage expansion (as illustrated in Witherspoon and Windsor) with the cultural disquiet over the importance of the nature and meaning of marriage, this article illuminates estate distribution conflicts in the context of the paradox of contemporary American socio-legal marriage culture. Despite the pop culture confusion over marriage, this article demonstrates why it is still the best default for estate planning conflicts.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".