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Record W7035909724

After Hours: Agency and Identity in Toronto’s Do-It-Yourself (DIY) Electronic Music Scene

2022· dissertation· en· W7035909724 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Identity (music)Electronic musicSpace (punctuation)ArchitectureReal estateQueerEstate
DOInot available

Abstract

fetched live from OpenAlex

The architecture of a night - a party lasting until the sun breaks, and the crowd of faces disperse, without a trace. What happens in that one night, however, is part of a much longer story about community growth, where small-scale venues encourage experimentations with identity. \n \nDo-It-Yourself (DIY) electronic music events inhabit spaces not zoned for nightclub or venue use, making them technically illegal. Meanwhile, the legal zones for nightclubs are some of Toronto’s most expensive real estate creating a high barrier to entry. Unlike large-scale nightclubs, DIY events enable organisers a high degree of curation, allowing them to define new social rules. Increasing access to DIY venues creates opportunities for underground musicians and artists to practise and perform. Their community-led nature suits smaller establishments with more intimate crowds. Resultantly, the DIY dancefloor becomes a space where queer and equity-seeking guests can participate comfortably, free from judgment and harassment. \n \nAfter Hours centres a series of conversations about the experiences and desires of DIY attendees, referencing events in Montreal, London, Tokyo, Berlin, and Toronto. These conversations are fragments of a larger series of eighteen zines that illustrate the socio-spatial dynamics of DIY spaces for future practitioners. Adding to the growing discourse on safety, identity, and gender in urban nightlife, this thesis explores community-led inhabitation and agency. How can spatial and social scale enhance perceived comfort in nighttime spaces?

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.021
Scholarly communication0.0110.002
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.012
GPT teacher head0.228
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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