"Watching the room where the music is happening": \nAn Examination of Live Streaming in the Vancouver Independent Music Scene During the Global Pandemic
Bibliographic record
Abstract
Before the COVID-19 pandemic, the city of Vancouver, B.C. has long been home to a thriving independent music scene. However, with the onset of the COVID pandemic, musicians lost their main source of income and means of communication with the audience: live performances. As a consequence, from 2020 to 2022, digital platforms and social media became music venues of the new age, and a primary way for artists to stay connected with listeners and each other. Thus, COVID-19 challenged the relationship between digital and physical spaces and made online mediums the key ‘performance spaces’ for both mainstream and independent artists. \nIn this Master’s thesis, I examine how live streams became the dominant music venues in 2020 and explore the way these streams remade social and physical connections as well as the relationships between physical and digital spaces within the Vancouver independent music scene during the global pandemic. This study is inductive, qualitative, and exploratory in its orientation, and the main research method is a series of semi-structured interviews with the local independent music scene members who have been especially active on streaming platforms during COVID. The study finds that while live streaming has been a critical medium for performance during the pandemic, musicians view it as a complement rather than a replacement or substitute for in-person performance concerts, owing to the distinct dynamics and aesthetics of the medium. The findings underscore the critical place of concert venues continue to hold for the Vancouver scene and the need for greater government support for sustaining these physical spaces as well as for leveraging new technologies and virtual spaces. \nAs there is currently limited literature touching upon the connection between music and \nCOVID-19, this study contributes to understanding what this crisis has \nmeant for the music industry, and how musical artists have been able to adapt during the \nchallenging times. Most significantly, an examination of the current experiences with the ‘digital’ lends insight into possible future development trajectories for the city’s music scene and the kinds of policies that can support Vancouver’s independent music scene.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".