The New Political Environment in Aging: Challenges to Policy and Practice
Bibliographic record
Abstract
The last quarter-century has seen a notable shift in the context of social policy as it relates to older adults in the United States and those who work with them. Critical dimensions in this shift include changes in the size and makeup of today's older population, the rise of conservatism in contemporary U.S. politics, and the more central place older Americans are coming to assume in policymaking around a host of social and economic policy issues. After briefly reviewing these contextual developments, the author presents 5 challenges they bring to social workers and other professionals working with the aged. Each of these reflect changing expectations, opportunities, and options confronting both policymakers and older people themselves as the dynamics of aging politics and policy evolve in ways that would have been hard to imagine 25 or 30 years ago.
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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 | 0.064 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.057 |
| Scholarly communication | 0.034 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.024 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 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".