Predicting who takes music lessons: parent and child characteristics
Bibliographic record
Abstract
Studies on associations between music training and cognitive abilities typically focus on the possible benefits of music lessons. Recent research suggests, however, that many of these associations stem from niche-picking tendencies, which lead certain individuals to be more likely than others to take music lessons, especially for long durations. Because the initial decision to take music lessons is made primarily by a child's parents, at least at younger ages, we asked whether individual differences in parents' personality predict young children's duration of training. Children between 7 and 9 years of age (N = 170) with varying amounts of music training completed a measure of IQ. Their parents provided demographic information as well as ratings of their own and their child's Big Five personality dimensions. Children's personality traits predicted duration of music training even when demographic variables and intelligence were held constant, replicating findings reported previously with 10- to 12-year-olds and 17-year-olds. A novel finding was that parents' openness-to-experience predicted children's duration of training, even when characteristics that pertained to children (demographic variables, intelligence, and personality) were controlled statistically. Our findings are indicative of passive and active gene-environment correlations, whereby genetic predispositions influence the likelihood that a child will have certain experiences, such as music training.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".