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Record W4248391896 · doi:10.32920/ryerson.14644521.v1

Exploring pedagogical practices for engaging boys in ballet

2021· preprint· en· W4248391896 on OpenAlexaff
Laura Feltham

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentUniversity of Ottawa
Fundersnot available
KeywordsBalletInclusion (mineral)Classical balletPsychologyPedagogyExpression (computer science)Best practiceMedical educationDanceVisual artsSocial psychologyArtPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

This research explores the pedagogical practices employed by ballet instructors for engaging boys in ballet. It also examines inclusion practices for gender non-conforming children in ballet, using principles of inclusion to make recommendations for instructors to employ with all children. Four current ballet instructors shared their experiences in semi-structured interviews. An overarching finding involved the role of parents in engaging boys in ballet and in creating more inclusive practices. Findings indicate that parent education is needed for more boys to be presented with ballet as an option. With regard to teaching practices involving gender non-conforming children, participants noted the need for parents to support an inclusive environment, and be open to their child’s gender expression in order for inclusive practices to be implemented. This paper presents recommendations for ballet instructors to create more welcoming environments for all students and suggestions for implementing gender-inclusive 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 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)0.009
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.749
GPT teacher head0.487
Teacher spread0.262 · 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
Published2021
Admission routes1
Has abstractyes

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