Aging and Self-Discrepancy: Evidence for Adaptive Change Across the Life Span
Bibliographic record
Abstract
BACKGROUND/STUDY CONTEXT: Higgins' self-discrepancy theory (SDT; Higgins, 1987, Psychological Review, 94, 319-340) postulates that individuals are motivated to decrease the discrepancy between their current and future selves. The objective of the current research was to investigate adult age differences in quantitative and qualitative aspects of self-discrepancy. METHODS: Higgins' self-guide strength measure (Higgins et al., 1997, Journal of Personality and Social Psychology, 72, 515-525) was utilized to compare self-discrepancy in older (aged 65-84) and younger (aged 17-30) healthy, community-dwelling adults. Additionally, the possible selves generated in the task were analyzed thematically. RESULTS: Age was associated with lowered expectations concerning both current and future selves, but the magnitude of self-discrepancy remained constant across the life span. Thematically, interpersonal-related possible selves were important for both age groups, whereas significant age differences emerged in several other thematic domains: younger adults generated significantly more related to achievement, whereas older adults were significantly more concerned with duties, obligations, and health. DISCUSSION: These findings reflect adaptive age-related changes in expectations and motivational priorities in line with life span theories of development.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".