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Record W2737523001 · doi:10.1386/dmas.2.2.119_1

Learning to let go: Phenomenologically exploring the experience of a grip and release in salsa dance and everyday life

2015· article· en· W2737523001 on OpenAlexaff
Rebecca Lloyd

Bibliographic record

VenueDance Movement & Spiritualities · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDancePsychologyPerceptionEmotiveGestureMerge (version control)AestheticsPhenomenology (philosophy)Openness to experienceEveryday lifeCognitive psychologyCommunicationSocial psychologyEpistemologyComputer scienceArtVisual artsArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Within the context of salsa dance, an exemplar that is highly attuned to gestural communication, there are moments when a perceptual merge, what Csikszentmihalyi posits as flow, is experienced. Movements are not anticipated, rather the fullness of the moment ripples out in fluid responsiveness to miniscule pressures and undulations in the simplest of gestures. Yet, achieving such a flow of reciprocity is not easy, as one must move not from a cognitive place of intention, but rather from a somatic sensibility premised on a corporeal openness. This enquiry thus explores what it is like to let go of habitual tensions that stand in the way of gestural communication within the context of salsa dance, and in so doing, the depth of connection to which attention is drawn represents the intertwining capabilities of relationships between bodies in any relational merge. And in delving into the nuances of gestural fluid responsiveness as guided by Daniel Stern’s notion of emotive motility living within the present moment, we may explore what it is like to form and feel a perceptual connection and the meaning that such moments hold, particularly for those who wish to heal and transform their daily relational existence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.429

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.257
Teacher spread0.153 · 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.

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

Citations3
Published2015
Admission routes1
Has abstractyes

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