Bibliographic record
Abstract
Underground music refers to genres which are not mass disseminated in the same way as mainstream styles, often due to their abrasive and lo-fi aesthetic. In large cultural centres, such as Toronto, underground music listeners develop their own communities and infrastructure – the framework of musical scenes – and seek out likeminded music fans both locally and virtually. Scholars such as Will Straw, Sarah Cohen and Holly Kruse have all developed theories concerning the development and definition of musical scenes which, while helpful, do not sufficiently examine how scenes are created and interact beyond geographically shared space. To address this gap, I have developed a theory which posits all musical scenes can be placed in one of two categories: local scenes, which are based around a specific locality and the infrastructure available for local scene participants, or super-local scenes, which are not bound by any one locality and can consist of multiple local scenes as well as independent participants. This presentation defines the Toronto underground scene as a super-local scene comprised of several smaller music scenes in the GTA. I will analyze the interactions between these local scenes, as well as with non-local participants, touring international acts, the mass media and the city authorities in order to model the structure of the super-local Toronto underground music scene. I suggest this framework will be useful to other scholars, even those outside of musicology, who are studying similar types of communities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".