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Record W4283458120 · doi:10.36510/learnland.v15i1.1059

Biodanza and Other Dance Forms as a Vehicle for Social-Emotional-Learning in Schools: A Scoping Review

2022· review· en· W4283458120 on OpenAlexaffvenue
Joana Calçada, Chris Gilham

Bibliographic record

VenueLEARNing Landscapes · 2022
Typereview
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsDancePsychosocialPsychologySocial emotional learningMental healthDance educationDevelopmental psychologyPedagogyApplied psychologyVisual artsPsychotherapistArt

Abstract

fetched live from OpenAlex

In this scoping review, the literature on the relationship between dance and Social Emotional Learning (SEL) was consolidated. Research of various forms of dance on psychosocial outcomes among K-6 populations included: increased sense of belonging, violence prevention, and increased creative thinking and problem-solving responses. The dance form, Biodanza, in particular, may be useful in engaging students in the active practice of social skills, appropriate use of body language, self-regulation, self-awareness, and boundaries. We suggest that using dance as a tool for SEL in the classrooms may contribute to improved student mental health and to more inclusive schools.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.076
GPT teacher head0.388
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2022
Admission routes2
Has abstractyes

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