Relative age effect in formal musical training
Bibliographic record
Abstract
Access to musical training depends on various factors, such as socioeconomic status and musical background of families, and the child's interest in learning music (related to their openness to experience). In the present study, we show an additional source of selection bias that has gone unnoticed: the relative age of children within the same cohort, when a selection process is implemented. The consequences of this grouping are known as the relative age effect, ranging from academic outcomes to self-esteem. In youth sports, there has been observed an overrepresentation of athletes born in the two first quarters compared to those born later. This study shows a similar unbalance across Spanish music conservatory courses in two samples: a Primary Sample of participants assessed by our research group ( N = 322; 33 % of children born in the first quarter vs. 21 % in the fourth quarter, V = .12) and a Secondary Sample comprised by the complete census of six conservatories in Spain ( N = 2182; 27 % vs. 24 %, V = .04). This bias was larger when computed on those participants selecting the most popular instrument. In our sample, the relative age of the children and adolescents was independent of other sources of selection bias, such as socioeconomic status. Moreover, the relative age effect was stable across conservatory courses, pointing to an enrolment bias and the impact of a lack of adjustment in the conservatory entrance exam.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".