Cohort Differences and Similarities in Women's Attitudes About Self and Aging
Bibliographic record
Abstract
Abstract This panel focuses on four complementing and international views of women’s aging, with a special emphasis on cohort comparisons and using three different studies of women, with contrasting methodological frameworks. In so doing, we present evidence related to trends in social percepetions of aging, attitudes about aging and identity, and ideas about control and objectification. Dr. Newton presents data on older Canadian women showing the connection between physical aging and identity maintenance, using both qualitative and quantitative data and using the lifecourse perspective. Dr. Ryan, using data from the Health and Retirement Study to compare cohorts of women from the 2008 and 2018 HRS waves, reports cohort differences in negative self-perceptions of aging, and that both cohort and negative self-perfections are associated with life satisfaction, using the life course developmental framework. Ms. Tran compares younger and older cohorts of women on a measure of self-objectification, finding that the older cohort reported lower objectification, consistent with a selection, optimization, and compensation (SOC) model. Finally, Dr. Sherman, using the same data set as Ms. Tran, shows that control beliefs are associated with objectification, regardless of cohort, consistent with objectification theory predictions of consistency over time regarding the impact of objectification experiences. Dr. Jamila Bookwala will provide discussion of this group of papers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".