Passing on: Personal attributes associated with midlife expressions of intended legacies.
Bibliographic record
Abstract
Expressions of the intent to leave behind something when we die can contain elements of both selflessness and selfishness. In this paper, we identify 3 different types of expressed legacy (personal, broader, and composite), and distinguish between them by examining their correlates (generativity, narcissism, and community involvement), as well as differences in expressed legacies for midlife African Americans and European Americans. Quantitative and qualitative data from surveys and interviews were drawn from the Foley Longitudinal Study of Adulthood (FLSA; N = 138; aged 55-58). We examined the contributions of generativity, narcissism, community involvement, and SES to each legacy, as well as the comparative levels of common significant predictors for each legacy, and the comparative likelihood of expressing particular legacies by race. Quantitative analyses showed that a different constellation of correlates predicted each legacy. Additionally, African Americans were more likely than European Americans to express legacies that indicated community involvement. Qualitative analyses showed that legacy groups (and races) also differed in open-ended responses encompassing personal concerns, talents, and goals. These findings highlight some of the mechanisms and correlates of how the intent to leave a legacy can provide meaning and purpose for midlife African Americans and European Americans. Results are discussed in light of previous research concerning how legacies are transmitted, and potential differences in cultural roots and meaning for African Americans and European Americans.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".