Bottom‐up or top‐down? Examining global and domain‐specific evaluations of how one's life is unfolding over time
Bibliographic record
Abstract
OBJECTIVE: We employed bottom-up and top-down perspectives to evaluate the link between how individuals view their lives as unfolding over time overall and in multiple life domains. METHOD: Participants from an American adult lifespan sample (n = 1,003, mean age = 54.39 years, 49% female, 94% Caucasian) evaluated their recollected past, current, and anticipated future satisfaction in seven life domains (health, work, finances, contribution to others, relationships with one's children, close relationships, sex life) and for their lives overall (life satisfaction) at two time points separated by 9 years. RESULTS: Mean-level trends varied by life domain and age. In cross-sectional and longitudinal analyses, domain-specific beliefs about changes in one's life explained substantial amounts of variance in perceived changes in overall life satisfaction, and multiple domains had unique predictive effects. Domain-specific beliefs also substantially mediated the predictive effect of age on perceived change in life satisfaction. CONCLUSIONS: Findings provide consistent support for the bottom-up perspective and limited support for the top-down model. Accordingly, individuals seem to derive beliefs concerning how their lives overall are unfolding over time based on perceived changes across multiple life domains.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".