Bibliographic record
Abstract
Musical meaning is multifaceted. It is highly sensory and yet often abstract; able to cross cultural boundaries and yet embedded in specific traditions. For the most part music as a semiotic system is characterized by non-referential meaning (Monelle, 1991). Nevertheless, in so-called programmatic music, musical themes are intended to refer to worldly objects and events on the basis of iconic (and indexical) grounds. Such non-arbitrariness has been extensively documented in the case of speech as well (Ahlner and Zlatev, 2010; Sonesson 2013; Imai and Kita, 2014).
 In an experimental study, I investigated how referential iconicity in speech operates in comparison to music, considering the factors (a) primary/secondary iconicity and (b) linguistic/cultural background. In the experiment 21 Swedish and 21 Chinese native speakers had to match musical fragments from Prokofief’s Peter and the Wolf and spoken word-forms to objects, represented by schematic pictures. The experiment was designed to have two conditions to operationalize higher degree of primary and secondary iconicity, respectively.
 The results showed that there was no significant difference between the overall results for music and linguistic tasks, indicating that the cognitive-semiotic processes involved are not limited to a single cognitive domain or semiotic system. Both groups performed significantly above chance in both conditions, which serves as a clear indicator that interpreting referential music in music and speech sounds is not purely a case of secondary iconicity.
 
 Author Biography
 Verónica Giraldo’s academic background is in music and linguistics. She holds an MA in Language and Linguistics with specialization in Cognitive Semiotics from Lund University. The work presented derives from her master’s thesis project. One of her main interest is exploration of the possible correlations between language and music from the perspective of cognitive semiotics. She is currently researching on how to make visual art and museums more accessible to the blind and visually impaired community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".