Alternative Music Culture in Toronto: Challenging Conventional Music Appreciation and Understanding
Bibliographic record
Abstract
Since modern art tends to be enigmatic and elusive at best, the public feels discouraged to seek out new artistic paradigms. Many new and alternative music genres, therefore, have failed to challenge conventional music paradigms, ultimately failing to infiltrate public school music curricula. In Toronto’s music scene, however, there have been several musical genres which have advanced the limits of conventional musical boundaries, particularly New Music and Acoustic Ecology, respectively represented by compositions such as “Made in China,” by Toca Loca, and “Streetcar Harmonics,” by Andra McCarthy. New Music is concerned with providing new listening tools to appreciate altered musical boundaries, whereas Acoustic Ecology focuses upon appreciating sounds in relationship to life and society. Ultimately both genres separately advocate cultures which strive to present an alternative way of listening to sound (be it music or soundscape), as well as challenging conventional musical paradigms, reinventing the notion of contemporary music, and questioning the boundaries of what ‘music’ really is, which has huge implications for music education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".