Bibliographic record
Abstract
Two of the most salient features of music are the blending of pitch and the matching of time. I propose here a possible evolutionary precursor of human music based on a process I call “contagious heterophony”. Heterophony is a form of pitch blending in which individuals generate similar musical lines but in which these lines are poorly synchronized. A wonderful example can be found in the howling of wolves. Each wolf makes a similar call but the resultant chorus is poorly blended in time. The other major feature of the current hypothesis is contagion. Once one animal starts calling, other members of the group join in through a spreading process. While this type of heterophonic calling is well-represented in nature, synchronized polyphony is not. In this article, I discuss evolutionary scenarios by which the human capacity to integrate musical parts in pitch-space and in time may have emerged in music. In doing so, I make mention of neuroimaging findings that shed light on the neural mechanisms of vocal imitation and metric entrainment in humans, two key processes underlying musical integration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".