Age trends in musical preferences in adulthood: 1. Conceptualization and empirical investigation
Bibliographic record
Abstract
This article aims to fill some gaps in theory and research on age trends in musical preferences in adulthood by presenting a conceptual model that describes three classes of determinants that can affect those trends. The Music Preferences in Adulthood Model (MPAM) posits that some psychological determinants that are extrinsic to the music (individual differences and social influences), and some that are intrinsic to the music (the perceived inner properties of the music), affect age differences in musical preferences in adulthood. We first present the MPAM, which aims to explain age trends in musical preferences in adulthood, and to identify which variables may be the most important determinants of those trends. We then validate a new test of musical preferences that assesses musical genres and clips in parallel. Finally, with a sample of 4,002 adults, we examine age trends in musical preferences for genres and clips, using our newly developed test. Our results confirm the presence of robust age trends in musical preferences, and provide a basis for the investigation of the extrinsic and intrinsic psychological determinants of musical preferences, in line with the MPAM framework.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".