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Record W4307621464 · doi:10.5539/res.v14n4p26

Biodanza and the Implementation of the Principle of Biocentric Education in Kindergartens

2022· article· en· W4307621464 on OpenAlexvenueno aff
Gila Cohen Zilka

Bibliographic record

VenueReview of European Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyGestureQualitative researchAtmosphere (unit)Developmental psychologyPedagogySociologySocial scienceGeographyLinguistics

Abstract

fetched live from OpenAlex

This study examined the introduction of biodanza to kindergartens together with the implementation of the principle of biocentric education, with emphasis on affectivity, to create an affectionate climate and to encourage meaningful interactions between children, and between the kindergarten staff and the children. The research question was: How would introducing biodanza and implementing biocentric education in kindergartens achieve these objectives? This was a qualitative study. The data were collected in Israel in the years 2017-2019. The study findings show that biodanza in kindergartens allowed for situations that required children to deal with emotional and social aspects of their interactions. As a result, positivity resonance in the kindergarten intensified greatly, and the atmosphere became more and more affectionate, accepting, and sharing, and positive gestures increased substantially. Communication between the kindergarten staff and the children underwent a change and became considerably more affectionate, compared to what it had been at the beginning of the process.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.404
Teacher spread0.355 · 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 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
Published2022
Admission routes1
Has abstractyes

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