Bibliographic record
Abstract
Key changes have long been employed in rock music to great dramatic effect. This paper takes as its point of departure the premise that modulations constitute “marked” events, which provide fertile ground for narrative analysis. Specifically I demonstrate, through analysis, the profitable intersection of ideas of musical narrative on the one hand (Burns and Woods 2004, Almén 2008, Burns 2010, etc.) and, on the other hand, current understandings of modulation in rock music (Capuzzo 2009, Doll 2011, and Temperley 2011b). Acknowledging the elusive nature of one-to-one correspondences between musical narrative and the patterning of pitch materials, my analyses instead seek to highlight relevant analyticalquestions. Six songs are considered as examples: “42” (Coldplay), “One Foot” (Fun.), “Hay Loft” (Mother Mother), “Knights of Cydonia” (Muse), “Across the Sea” (Weezer), and “Everlasting Everything” (Wilco). These demonstrate a range of situations, from passages in which a modulation away from a song’s initial tonic key occupies only a few measures to complex tonal trajectories that engage the majority of a song. The conclusion suggests five potential archetypes, each describing a different narrative function that may be supported by modulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".