Perceptual evaluation of violins: A psycholinguistic analysis of preference verbal descriptions by experienced musicians
Bibliographic record
Abstract
In this paper, how the notion of violin quality is conveyed in spontaneous verbalizations by experienced violinists during preference judgments is investigated. The aims of the study were to better understand how musicians conceptualize violin quality, what aspects of the sound and the playing experience are essential, and what associations are formed between perceptual evaluation and physical description. Upon comparing violins of varying make and age, players were interviewed about their preferences using open-ended questions. Concepts of violin quality were identified and categorized based on the syntactic and linguistic analysis of musicians' responses. While perceived variations in how a violin sounds and feels, and consequently conceptualization structures, rely on the variations in style and expertise of different violinists, the broader semantic categories emerging from sensory descriptions remain common across performers with diverse musical profiles, reflecting a shared perception of physical parameter patterns that allowed the development of a musician-driven framework for understanding how the dynamic behavior of a violin might relate to its perceived quality. Implications for timbre perception and the crossmodal audio-tactile sensation of sound in music performance are discussed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".