An outline of the narrative grammar of electronic dance music
Bibliographic record
Abstract
We argue that electronic dance music (EDM) exhibits a parallel structural organization to that which has been proposed for cartoons (comics) after the model of hierarchical structure proposed in theoretical linguistics. According to this parallel, both systems are governed by general cognitive mechanisms for the narrative organization of tension and release, which are not modality-specific. We show that notions from visual narrative analysis, such as an Establisher–Initial–Peak–Release template, can be applied directly to EDM tracks as an Intro/Breakdown–Buildup–Core–Outro/Cut template. In doing so, we focus on how to formally define and operationalize relevant notions such as Breakdown, Buildup, and Core. As part of our analysis, we show that the scene-setting Establisher segments of visual narratives map onto two distinct categories in EDM: they correspond to intro sections at the beginning of a track and to breakdown sections in the middle of a track; we strengthen the analogy to visual narrative analysis by introducing refinements such as a pre-drop break that often occurs at the end of a buildup segment. To adjudicate between competing hypotheses on the hierarchical structure of a given EDM track, we demonstrate that analytical tests from linguistics and visual narrative analysis can be successfully applied. By introducing these analytical tools, this article sets the stage for further explorations in the linguistically informed analysis of the structure and meaning of EDM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".