Trying to have fun in ‘No Fun City’: Legal and illegal strategies for creating punk spaces in Vancouver, British Columbia
Bibliographic record
Abstract
Abstract Music scenes are sites of a reciprocal relationship that exists between physical spaces and the bodies that occupy them. Music venues, as centralizing nodes within local punk scenes, are integral spaces of socialization in which scene participants actively produce identity, culture and community. If scene participants and the physical venues that they congregate in are engaged in such an active, inter-dependent relationship, then what happens to those participants and their established communities when music venues are shut down? When faced with the dissolution of these gathering places, punk scene participants utilize various spatial tactics of rebuilding or transforming space, even if only a temporary measure to address the demands of the community at large. These strategies may work within established legal guidelines or outside them in an attempt to escape the threat of surveillance. However, additional difficulties may occur within the scene itself when participants disagree not only on which strategy is best but also what type of space most closely conforms to their understanding of what exactly ‘punk’ is or should be. This article conducts a comparison of such strategies via two case studies located within Vancouver, British Columbia: (1) The Cobalt Hotel; and (2) The Safe Amplification Site Society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".