Evaluating violin quality: How consistent are skilled players?
Bibliographic record
Abstract
A perceptual experiment was designed to investigate how consistent violinists are at evaluating violin quality. The objective was to examine both intra- and inter-subject consistency across a certain range of violins. Skilled classical violinists were asked to play a set of different violins, evaluate their quality, and order them by preference. Violins of different periods were used, varying from student to performance level. Low light conditions and dark sunglasses were used to hide the identity of the instruments as much as possible. Considering the bow as an extension of the player, violinists carried out the task using their own bow. Upon completing the task, participants had to comment on the ranking process and provide rationale for their choices. Preliminary results from a pilot study indicate that both intra- and inter-subject consistency are high when violinists are presented a small group of instruments that are fairly distinct from one another. Results of a more in-depth study (with more instruments, some very similar to one another) will be presented at the conference.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".