I COULDN'T MAKE A PIECE AS BEAUTIFUL AS THAT: A CONVERSATION WITH ALLISON CAMERON
Bibliographic record
Abstract
Abstract The composer Allison Cameron (b. 1963) lives in Toronto. Her music has been widely performed at festivals such as Emerging Voices in San Diego, Evenings of New Music in Bratislava, Festival SuperMicMac in Montréal, Newfoundland Sound Symposium, New Music across America, Bang on a Can Marathon in New York, New York, and Rumori Dagen in Amsterdam. A dedicated performer of experimental music in Toronto, Allison co-founded the Drystone Orchestra (1989) and the Arcana Ensemble (1992). She has been improvising since 2000 on banjo, ukulele, cassette tapes, radios, miscellaneous objects, mini amplifiers, crackle boxes, toys and keyboards, in collaboration with Éric Chenaux, the Draperies, Ryan Driver, Dan Friedman, Mike Gennaro, Kurt Newman, John Oswald, Stephen Parkinson and Mauro Savo, among other musicians. In that same year she became Artistic Director of Toronto's experimental ensemble Arraymusic, a position she held for five years. In 2007, she founded the Allison Cameron Band with Eric Chenaux and Stephen Parkinson, and in 2009, the trio c_RL with Nicole Rampersaud (trumpet) and Germaine Liu (drums). Allison has experimented with graphic and notational scores that will soon be gathered and published as a collection. Additionally, she is the winner of the 2018 KM Hunter Award for music in Ontario.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.048 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".