Categorization and lexicon in verbal descriptions of violin quality by performers
Bibliographic record
Abstract
This work aimed to explore how violin quality is conceptualized as reflected in spontaneous verbal descriptions by experienced performers collected while playing in a perceptual evaluation experiment. Participants performed a preference ranking task and justified their perceptions in a free verbalization task. Using the constant comparison analysis from grounded theory, a concept map was developed, which can be useful for future studies aimed at assessing violin qualities. A psycholinguistic analysis of the quality-descriptive lexicon used by violinists further revealed a variety of linguistic devices referring to either the sound of a violin or to the violin itself as the cognitive objects. Adjectives for the description of sound characteristics are largely borrowed from four semantic fields related to texture-temperature (smooth vs. rough), action-presence (resonant vs. muted), size-volume (deep vs. flat), and light (dark vs. bright). These semantic fields indicate what type of dimensions may explain the perception of violin timbre, contributing to the area of violin acoustics research as well as to the broader area of timbre research. Some acoustical interpretations are discussed in the context of finding correlations between measurable vibrational properties of a violin and its perceived quality.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".