Pacing, Performance, and Perception in Alice Ping Yee Ho’s <i>Angst</i>
Bibliographic record
Abstract
This paper examines how performer decisions regarding musical pacing and expressive timing can shape the communication of affective states in a musical work, through a case study of Angst (Ho, Alice Ping Yee. Citation2000. Angst. Canadian Music Centre), composed by Alice Ping Yee Ho and premiered by soprano Janice Jackson. Described by Ho as a ‘reaction to the myth of the inferiority of women’, this work for solo soprano ‘express[es] the fear and anxiety that women encounter’. Angst includes fluctuating time signatures, long-held notes of varying durations, and recurring motives that enter at unpredictable times, which, when combined, can complicate a listener's ability to discern consistent metre and/or predict future musical events.Using a video recording of Angst, I examine how Jackson's performance conveys the anxiety expressed by the protagonist. I draw on Danuta Mirka's adaptation (Mirka, Danuta. Citation2009. Metric Manipulations in Haydn and Mozart: Chamber Music for Strings, 1787–1791. Oxford: Oxford University Press) of Ray Jackendoff's parallel multiple-analysis model for determining metre (Jackendoff, Ray. 1991. “Musical Parsing and Musical Affect.” Music Perception 9 (2): 199-230), David Huron's theory of musical expectation (Huron, David. Citation2006. Sweet Anticipation: Music and the Psychology of Expectation. Cambridge, MA: MIT Press), and Austin Patty's pacing scenarios (Patty, Austin T. Citation2009. “Pacing Scenarios: How Harmonic Rhythm and Melodic Pacing Influence Our Experience of Musical Climax.” Music Theory Spectrum 31 (2): 325-367), and I expand on this work by discussing the affective impact of pacing. I suggest that Jackson's performance of Ho's score creates a confounding listening experience that reflects the work's themes of anxiety, fear, and realisation of power.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".