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Prelude

2023· book-chapter· en· W4387871688 on OpenAlexaff
Colin Andrew Lee, kei slaughter, Natasha Thomas

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsQueerAestheticsContext (archaeology)NarrativeMusic therapySociologyGender studiesPsychologyArtHistoryLiteraturePsychotherapist

Abstract

fetched live from OpenAlex

Abstract As an emerging approach in the 21st century, queer and trans music therapy (QTMT) challenges perspectives and narratives from ethnocentric and cisheteronormative traditions, which have dominated the field of music therapy. Raising the essential question of what it means to create queer and trans spaces in music therapy, this chapter presents an open discourse on the need for change and new beginnings. How can music therapists, allied professionals, and artists become more aware of and sensitive to music as a queer and trans inventive force? This chapter provides context for the handbook as a whole. It includes a brief literature review on QTMT, perspectives on Black queer and trans music therapy, and an introduction to the value of queer musicking. Considering QTMT as a creative process, this chapter also presents a foundational definition of practice that would be purposefully mindful of queer and trans concerns and issues. Just as music inspires and touches us in profound ways, so can queer musicking perform as a therapeutic force to instigate positive human change in QTMT practice.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.217
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0070.005
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2170.123

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 head0.059
GPT teacher head0.193
Teacher spread0.134 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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