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Record W1002990502

Uncovering the Essence of What Animates Us Beneath The Dance: Investigating the Lived Experiences of Bodily Perceptions Generated While Dancing

2013· dissertation· en· W1002990502 on OpenAlexaboutno aff
Karen McKinlay Kurnaedy

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typedissertation
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
Fundersnot available
KeywordsDancePerceptionAestheticsLived experienceVisual artsPsychologyArtPsychoanalysisNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

This dissertation is a phenomenological study in which I endeavour to uncover the essence of what animates us beneath the dance by investigating the lived experiences of bodily perceptions generated while dancing. I present four perspectives to illuminate this essence. In my first perspective, I offer literature from a variety of phenomenologists, dancers and choreographers which substantiate vital and spiritual perceptions generated while dancing. In my second perspective, I conduct an autobiographical inquiry and recollect my childhood experiences of dance. In my third perspective, I present an historic narrative about the lives of my dance mentors Gertrud and Magda Hanova which is of significant historical importance for dance history to Vancouver. In my fourth perspective, looking through the lens of phenomenology and drawing on performative inquiry, I present textual reflections written by grade four students, which explore the children’s dancing experiences. In conclusion, I stress the need to return again and again to the dancing body in order to experience revitalization and renewal. I explore the significance of dance for teaching and learning and expand on how this study may inform education. This work is an invitation to celebrate returning to the dancing body and to honour the divinity of the dance in each person.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.032
GPT teacher head0.241
Teacher spread0.208 · 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 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
Published2013
Admission routes1
Has abstractyes

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