Identifying the Poorest Older Americans
Bibliographic record
Abstract
OBJECTIVES: Public policies target a subset of the population defined as poor or needy, but rarely are people poor or needy in the same way. This is particularly true among older adults. This study investigates poverty among older adults in order to identify who among them is financially worst off. METHODS: We use 20 years of data from the Consumer Expenditure Survey to examine the income and consumption of older Americans. RESULTS: The poverty rate is cut in fourth if both income and consumption are used to define poverty. Those most likely to be poor defined by only income but not poor defined by income and consumption together are married, White, and homeowners and have a high school diploma or higher. The income poor alone display sufficient assets to raise consumption above poverty thresholds, whereas the consumption poor are shown to have income just above the poverty threshold and few assets. DISCUSSION: The poorest among the older population are those who are income and consumption poor. Understanding the nature of this double poverty population is important in measuring the success of future public policies to reduce poverty among this group.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".