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Record W4213071310 · doi:10.3389/fspor.2022.795956

Women's Articulations of Aging: “Learning to Be Affected” Through Experiences in Recreational Ballet

2022· article· en· W4213071310 on OpenAlexafffund
Allison Jeffrey, Pirkko Markula, Corinne Story

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsBalletDanceRecreationClassical balletBallet dancerVisual artsAestheticsMaterialismPsychologyArtSociologyEpistemologyPolitical science

Abstract

fetched live from OpenAlex

In this article, we draw upon the experiences of mature recreational dancers who participated in classes facilitated by a professional ballet company and catered to older adults. Moving with 11 women through a 10-week ballet course, and immersing ourselves in the empirical material, we recognized opportunities for broadening our analysis of aging dancing bodies. Inspired by a Latourian understanding of bodies and a recent new materialist turn in humanities and social sciences, we became curious about the ways that the women were being affected by their experiences in ballet. The ballet studio, the barre, muscles, sweat, and music were all discussed as influential aspects contributing to their understandings of aging and dancing. Moving beyond biomedical prescriptions and extending socio-cultural constructions, we reveal opportunities for Latourian theory to dance with us toward re-imagining what is possible for aging recreational ballet dancers. Here, we allow the women's articulations of aging in ballet to exist as unique expressions unbound by limitations. Moving with women as they learn to become more affected through dance, we are given the opportunity to think about bodies, ballet and aging differently.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

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