Improvised Performances: Urban Ethnography and the Creative Tactics of Montreal’s Metro Buskers
Bibliographic record
Abstract
Buskers—street performers—evince the creative tactics of self-conscious agents who are both produced by and productive of the social and material conditions within which they carry out their practices. In this article, I discuss my ethnographic research among buskers in Montreal’s underground transit system—the metro—and examine their highly variable and improvisational practices (musical and spatial). I detail how buskers work with and against the constraints and possibilities posed by the material characteristics of those spaces (especially in terms of acoustics) as well as formal regulations and prevailing social norms. This suggests understanding busking as a relational process of “cobbling together” that is never entirely fixed or bounded, but dispersed and always in-the-making. Further, I demonstrate how the research process in this context is itself a creative, improvisational approach, guided as much by the conditions at hand as by an overarching research design. By drawing parallels between the busker-performer and my role as researcher and creative producer, particularly in my use of audio-visual production, I argue that ethnographic research is, itself, a form of assemblaging, of bricolage—an embodied, relational process that involves multiple participants (human and material) of varying influences, bound together by the tactical activities of the researcher.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".