Human Values Across the Lifespan: Age-Graded Differences at Three Hierarchical Levels and What We Can Learn From Them
Bibliographic record
Abstract
Personality-development research is flourishing. Here, we extend these efforts horizontally (new constructs) and vertically (new levels within the same construct) by charting out age-graded differences in Schwarz's human values across 80,814 individuals. Conducting a systematic investigation of cross-sectional age-graded differences in human values-from late teenage years to post-retirement-featuring 36 analytical model choices and 180,000 simulation-based decisions, our analyses replicate some earlier findings (e.g., increasing self- and growth-focus during adolescence and increasing security concerns during adulthood), while also highlighting complex and previously unappreciated dynamics. As such, while it is a common practice to aggregate specific values into parsimonious higher-order concepts to ease interpretation, this may risk overlooking meaningful trends in lower-order value development. Specifically, revealing unique and asynchronous patterns for value nuances, we find that aggregation (a) leads to a loss of critical information, (b) creates conflicting results when nuances diverge, and (c) significantly reduces predictive power.
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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 | 0.008 |
| 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 | 0.002 |
| 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".