Can You Tell a Prodigy From a Professional Musician?
Bibliographic record
Abstract
Little empirical research has been conducted on prodigies, in no small part due to the fact that there exists no agreed-upon definition with which to identify them. The most widespread definition characterizes a prodigy as a child who, at a very young age (typically before 10) performs at an adult professional level (Feldman & Goldsmith, 1986). We tested this definition by asking musicians and nonmusicians to (1) judge whether audio clips were played by a prodigy or a professional, and (2) identify which of two clips of the same piece was played by a prodigy. Listeners performed above chance in both tasks but by a very modest margin, and musicians performed better than nonmusicians. Their low performance implies that prodigies perform well enough to be judged in terms of the most demanding criteria of performance in the field. Yet older prodigies (11 to 14) were harder to distinguish from professionals than younger prodigies (under 10), suggesting a protracted developmental trajectory for prodigy performance. Furthermore, the rate at which prodigies progressed in their playing appears higher than for regular students, suggesting that rate of progress might be used as an additional criterion for defining music prodigy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".