Post-Quantization and Glitch in the Music of Nicole Lizée. Nicole Lizée in Conversation with Ben Duinker
Bibliographic record
Abstract
Award-winning composer and video artist Nicole Lizée creates new music inspired by an eclectic mix of influences including the earliest MTV videos, turntablism, rave culture, Hitchcock, Kubrick, Alexander McQueen, thrash metal, early video game culture, 1960s psychedelia, and 1960s modernism. She is fascinated by the glitches made by outmoded and well-worn technology and captures these glitches, notates them and integrates them into live performance. As a percussionist and chamber musician, I’ve had the pleasure of commissioning, touring and recording Lizée’s music, as well as performing it alongside her. Often synced to films created by the composer herself, Lizée’s compositions require devoted and sustained engagement from performers. This engagement rewards musicians and audiences alike by facilitating a deeper connection with the music––its crafting of the experience of time and stewardship of adrenaline and energy. Lizée gave a keynote address at the second Rhythm in 20 th Century Music Conference at McGill University, her alma mater, in September 2023. This interview reflects and elaborates on that talk.
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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 | 0.000 |
| 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".