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2023· book-chapter· en· W4318182963 on OpenAlexaffabout
Naomi Ben-Aharon, Mason Gibson, Tyler Reidy

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPrideShameNarrativeQueerIdentity (music)Music therapyPedagogyPsychologySociologyAestheticsGender studiesArtPsychotherapistSocial psychologyPolitical scienceLiterature

Abstract

fetched live from OpenAlex

Abstract This chapter is a philosophical and autoethnographic study by three queer-identifying Canadian former graduate music therapy students. Through the evaluation of personal narratives and journals, themes were created that highlighted the isolation, shame, safety, authenticity, othering, pride, and identity synthesis that we faced during our education as music therapy students. These themes inspired us to make recommendations for future music therapy pedagogies that would include implementation of anti-oppressive and affirming approaches to supervision and education. This chapter seeks to provide representation for queer and trans music therapy students, music therapists, professors, and supervisors as well as examining how universities can become safer and more inclusive spaces for learning.

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)0.002
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 score0.264
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0090.003
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2640.089

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 head0.120
GPT teacher head0.318
Teacher spread0.198 · 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 routes2
Has abstractyes

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