Growth is not just for the young: growth narratives, eudaimonic resilience, and the aging self
Bibliographic record
Abstract
In this chapter we present the case that growth is a central concern in older adults' self-identity, facilitating dispositional well-being and resilience in older adulthood. Contrary to the view that “growth is for the young and loss is for the old,” research on personal goals and memories demonstrates that older adults are at least as concerned with gain and growth as they are with loss. As for personal memories, we turn to quantitative research on narrative self-identity. Growth-oriented narratives are common in older adulthood. They predict well-being, differentiate hedonic from eudaimonic well-being, and differentiate two forms of eudaimonic well-being. Finally, we present a framework for studying resilience: hedonic resilience involves affect regulation in the wake of loss or potential trauma, whereas eudaimonic resilience includes affect regulation but additionally considers meaning regulation. Introduction Psychological resilience in old age, like in any period of adulthood, is intimately tied to self-identity. Some forms of self-identity are more likely than others to facilitate resilience across the lifespan (Greve and Staudinger,2006). For example, growth-oriented identities are more likely than others to precede increases in meaning-making and adaptation (e.g., Adler, 2009; Bauer and McAdams, in press; King and Smith, 2004; Pals, 2006b). In this chapter we argue that a growth orientation in one's self-identity serves as a central feature of the aging self and in doing so facilitates resilience. We make three claims about resilience and the aging self. First, growth is a normative, often central concern in older adults' personal goals .
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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 | 0.000 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".