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Record W2789465398 · doi:10.1386/punk.7.1.75_1

Trying to have fun in ‘No Fun City’: Legal and illegal strategies for creating punk spaces in Vancouver, British Columbia

2018· article· en· W2789465398 on OpenAlexaffabout
Katie Victoria Green

Bibliographic record

VenuePunk & Post Punk · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsTrent University
Fundersnot available
KeywordsPunkSpace (punctuation)SociologySocializationIdentity (music)Representation (politics)Media studiesPublic relationsLawAestheticsPolitical scienceHistoryArtComputer scienceSocial sciencePoliticsArt history

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes2
Has abstractyes

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