Singing proficiency in the general population
Bibliographic record
Abstract
Most believe that the ability to carry a tune is unevenly distributed in the general population. To test this claim, we asked occasional singers (n=62) to sing a well-known song in both the laboratory and in a natural setting (experiment 1). Sung performances were judged by peers for proficiency, analyzed for pitch and time accuracy with an acoustic-based method, and compared to professional singing. The peer ratings for the proficiency of occasional singers were normally distributed. Only a minority of the occasional singers made numerous pitch errors. The variance in singing proficiency was largely due to tempo differences. Occasional singers tended to sing at a faster tempo and with more pitch and time errors relative to professional singers. In experiment 2 15 nonmusicians from experiment 1 sang the same song at a slow tempo. In this condition, most of the occasional singers sang as accurately as the professional singers. Thus, singing appears to be a universal human trait. However, two of the occasional singers maintained a high rate of pitch errors at the slower tempo. This poor performance was not due to impaired pitch perception, thus suggesting the existence of a purely vocal form of tone deafness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".