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Record W3096104122 · doi:10.3138/ctr.184.010

<i>RADAR</i>: Challenging Colonial Soundscapes and Violence through Creation and Performance

2020· article· en· W3096104122 on OpenAlexvenueaboutno aff
Spy Dénommé‐Welch, Catherine Magowan

Bibliographic record

VenueCanadian Theatre Review · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSoundscapeIndigenousColonialismSociologyAestheticsSpace (punctuation)Visual artsGender studiesHistoryArtMedia studiesSound (geography)AcousticsArchaeologyLinguistics

Abstract

fetched live from OpenAlex

In this article, Spy Dénommé-Welch and Catherine Magowan examine how sound is used in the chamber work RADAR to create a visceral experience for the audience and musicians that addresses themes of violence, grief, and the effects of colonialism. The authors reflect on the historical and contemporary implications of music, including music in alternative and experimental spaces, and how traditional spaces are often inaccessible to racialized and/or marginalized musicians. Within this landscape, the authors examine how RADAR works to disrupt Eurocentric conventions of music and performance while responding to Canada’s role in the perpetuation of violence against Indigenous women, girls, and Two-Spirit peoples. In addition, Dénommé-Welch and Magowan share their collaborative process and articulate their rationales for RADAR’s structure and unconventional soundscape. Finally, the authors reflect on the rehearsal process leading up to the premiere of RADAR and how intercultural, decolonizing approaches are important strategies for engaging with musicians and making space for more inclusive artistic practices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.208
Teacher spread0.182 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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