Bibliographic record
Abstract
In this paper, I make the following claims: (1) Subjective experience is tremendously useful in guiding productive research. (2) Studies of auditory scene analysis (ASA) in adults, newborn infants, and non-human animals (e.g., in goldfish or pigeons) establish the generality of ASA and suggest that it has an innate foundation. (3) ASA theory does not favor one musical style over another. (4) The principles used in the composition of polyphony (slightly modified) apply not only to one particular musical style or culture but to any form of layered music. (5) Neural explanations of ASA do not supersede explanations in terms of capacities; the two are complementary. (6) In computational auditory scene analysis (CASA) – ASA by computer systems – or any adequate theory of ASA, the most difficult challenge will be to discover how the contributions of a very large number of types of acoustical evidence and top-down schemas (acquired knowledge about the sound sources in our environments), can be coordinated without producing conflict that disables the system. (7) Finally I argue that the movement of a listener within the auditory scene provides him/her/it with rich information that should not be ignored by ASA theorists and researchers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".