The Co-Development of Generativity and Well-Being into Early Late Life
Bibliographic record
Abstract
The current manuscript replicates and extends the few existing studies of generativity in later adulthood with regard to two aims: (1) to model individual differences in the development of generativity into early late life and (2) to quantify the relationship between development in generativity and development in well-being into late midlife and early older adulthood. Data from the Rochester Adult Longitudinal Study (RALS) are used to address these aims in a preregistered secondary analysis of existing RALS data (see https://osf.io/syp2u). Analyses quantify individual development of generativity in a sample of 284 RALS participants who completed the Loyola Generativity Scale (LGS; McAdams & de St. Aubin, 1992) and the Psychological Well-Being Scale (PWB; Ryff, 1989a) during the most recent two waves of the RALS (2000 – 2012). Both generativity and well-being demonstrated substantial rank-order stability and mean-level change as well as individual variability on both. Dual score change models showed a robust concurrent relationship between both constructs at the first assessment and meaningful correlated change between generativity and well-being over time. While demographic covariates were not associated with study findings, one of the most important limitations of the RALS is the racial and ethnic homogeneity of the sample that constrains generalizability to other racial and ethnic groups. Results are discussed in the context of our current understanding of the development and impact of generativity in later adulthood anddirections for future research in this area are identified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".