Music through the ages: Trends in musical engagement and preferences from adolescence through middle adulthood.
Bibliographic record
Abstract
Are there developmental trends in how individuals experience and engage with music? Data from 2 large cross-sectional studies involving more than a quarter of a million individuals were used to investigate age differences in musical attitudes and preferences from adolescence through middle age. Study 1 investigated age trends in musical engagement. Results indicated that (a) the degree of importance attributed to music declines with age but that adults still consider music important, (b) young people listen to music significantly more often than do middle-aged adults, and (c) young people listen to music in a wide variety of contexts, whereas adults listen to music primarily in private contexts. Study 2 examined age trends in musical preferences. Results indicated that (a) musical preferences can be conceptualized in terms of a 5-dimensional age-invariant model, (b) certain music-preference dimensions decrease with age (e.g., Intense, Contemporary), whereas preferences for other music dimensions increase with age (e.g., Unpretentious, Sophisticated), and (c) age trends in musical preferences are closely associated with personality. Normative age trends in musical preferences corresponded with developmental changes in psychosocial development, personality, and auditory perception. Overall, the findings suggest that musical preferences are subject to a variety of developmental influences throughout the life span.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".