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Record W3201352284 · doi:10.1080/14647893.2021.1980529

Dancing after homelessness

2021· article· en· W3201352284 on OpenAlexaff
Sylvie Fortin

Bibliographic record

VenueResearch in Dance Education · 2021
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsDanceThematic analysisFocus groupPsychologyAction (physics)Action researchPedagogyBiopsychosocial modelSet (abstract data type)SociologyQualitative researchSocial psychologyVisual artsSocial science

Abstract

fetched live from OpenAlex

Dance as an artform touches the biopsychosocial dimensions of people and as such is increasingly used as a vehicle to improve the quality of life of diverse populations in the community. Yet little is known about how dance classes are experienced by marginalized populations. The purpose of this action research was to explore the potential of dance to contribute to the recovery of women having experienced homelessness. The objectives were to describe: 1) the artistic content (the ‘what’) and the pedagogical approach (the ‘how’) of the classes, and 2) the experiences of all the people involved in the action research (women, workers at the women’s home, dance facilitators and researcher). Over a three-year period, weekly classes were offered in a women’s home. Data was collected through individual interviews, focus groups and observations. A thematic analysis revealed eleven categories of dance activities which were subsequently associated with indicators of recovery. The pedagogical approach was driven by a set of values clearly embedded in the classes; the facilitators deployed numerous adaptations to answers the women’s needs. Sufficient evidence supports the claim that dance is a promising practice for individual women’s process of recovery. Implications for future research and practice are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.113
GPT teacher head0.476
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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