Musical Meaning and the Semiotic Hierarchy: Towards a Cognitive Semiotics of Music
Bibliographic record
Abstract
Research on the meaning of music has a long tradition, with approaches from several fields, but it lacks a coherent framework for interdisciplinary discussions. As a result, the notion of meaning in music is fragmented among contrasting perspectives. I propose a cognitive-semiotic approach to the analysis of the meaning evoked by music listening, adopting a framework that eludes disciplinary limitations and expands the notion of meaning to the phenomenological concept of intentionality. For this purpose, I apply Zlatev’s Semiotic Hierarchy to the experience of listening to music, analysing the diversity of meaning-making processes involved in music as distributed among several layers of experience. As a result, I propose an updated version of the Semiotic Hierarchy, clarifying its structure as based on possibilities of meaning-making, and allowing for temporality to pervade experience throughout all layers. I highlight the connectedness and simultaneity of different kinds of intentionality, resulting in the addition of the dimension of aesthetic experience – which I analyze as characterizing culture-general music listening. A key claim is that experiencing music aesthetically articulates the listener’s body in their inner sense of space and time, making them feel a sense of movement and vitality. This grounds music as a semiotic system, connecting with and fostering virtually uncountable subject-relative and culture-specific meaning-making acts.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".