Canavangard, Udo Kasemets's<i>Trigon</i>, and Marshall McLuhan: Graphic Notation in the Electronic Age
Bibliographic record
Abstract
Abstract Composer Udo Kasemets (1919–2014) emigrated to Canada in 1951 from Estonia following the Second World War, and during the 1960s undertook a number of initiatives to mobilize experimental music in Toronto. This article investigates Canavangard, Kasemets's publication series of graphic scores which appeared between 1967 and 1970. Influenced by Marshall McLuhan's spatial theory of media, Kasemets saw the transformative potential of non-standard notational practices to recalibrate the relationships between composer, performer, and listener. Kasemets's 1963 compositionTrigon, which was frequently performed by his ensemble during the decade, illuminates the connections between McLuhan and experimental music. In my analysis of the work, I argue thatTrigonmanifestly puts into performance many of the rhetorical strategies used by McLuhan to describe the immersive, intersensory environments of post-typographic media ecologies. Kasemets believed that abandoning standard notation would have extraordinary ramifications for musical practice going forward in the twentieth century, similar to how McLuhan saw the messianic power of electronic media to destabilize the typographic universe. Canavangard, as much more than a short-lived publication series of graphic scores, maps the convergences of music, culture, and technology in post-war Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".