Bibliographic record
Abstract
Using the AHEAD study, this article examines the wealth distribution among American households with a member at least 70 years old. Household wealth is quite unevenly distributed among older American households. Those households in the top 10th percentile of the wealth distribution have 2,500 times as much wealth as those at the lowest 10th percent. This sharp wealth disparity relative to income dispersion is the dominant reason why older minority households have ac-cumulated so little wealth compared to White households. Wealth varies by a factor of seven to one when both spouses are in poor health compared to when they say that they are in excellent health. Finally, AHEAD data on bequest inten-tions suggest a bifurcated bequest motive. Most older households plan to bequeath a modest financial inheritance, but about one-quarter expect to leave inheritances worth $100,000 or more. THE process of asset accumulation and depletion at olderages is a central issue on which any evaluation of the well-being of the elderly population depends. Our current knowledge of this process is limited because most social sci-ence surveys either did not include wealth modules or mea-sured assets quite poorly. This problem is much more severe during the postretirement years because of inadequate sam-ple sizes in this age group in more general purpose surveys.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".