Bibliographic record
Abstract
Musicologists and philosophers have commonly attributed distinctive qualities to individual musical pitches, and absolute pitch (AP) possessors recognize and recall notes and keys with immediacy and accuracy, leaving little doubt that they are aware of such characteristics. Bachem proposed that these distinct tonal qualities underlie the rapid and accurate judgments that he identified as genuine AP, and he defined the qualities as tone chroma (TC). The TCs of notes and keys, and of notes separated by a musical octave ( pitch class) are frequently expressed in visual terms, and studies of synaesthesia, the association of intersensory stimuli, provide clues to the systematic variation of TC qualities. The historical literature relating to note and key characteristics is commonly overlooked in the study of AP, however, and the article seeks to address this problem. Long-standing conclusions are reviewed, leading to the hypothesis that TC sensitivity can derive from an awareness of variations in the acoustical beats that occur in the tuning of instruments to equal temperament and which are perceived with particular clarity in organ tuning. This acoustical hypothesis is supported by modern neuroscientific findings and was predicted by theoretical observations in the literature as long ago as three centuries. In a follow-up article, Thurlow and Baggaley discuss the role of synaesthesia-type judgments in musical skills not previously regarded as absolute: for example, a perfect touch capacity observed in keyboard players.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".