Bibliographic record
Abstract
This is an expanded version of comments on the future of the demography of aging at an invited session of the 2008 annual meeting of the Population Association of America. In an introduction, John Haaga offers reasons for a revival of interest in population aging, including greater realization of plasticity in aging trajectories at both individual and societal levels. Linda Martin proposes that population scientists working in aging emulate those studying fertility and family planning in previous decades, learning from interventions (in this case, aimed at increasing retirement savings and reducing disability at older ages). Changes in family structure will increasingly affect new cohorts of the elderly, and Linda Waite speculates on the ways in which changes in the economy, medicine, and the legal environment could affect the social context for aging. Research on mortality at older ages is “alive and well” asserts James Vaupel, who sets out six large questions on mortality trends and differentials over time and across species. Lastly, Wolfgang Lutz expands the scope of projections, showing the considerable uncertainty about the timing and pace of population aging in the developing world and the effects on future elderly of the increases in educational attainment in much of the world during the second half of the twentieth century.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.417 | 0.247 |
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".