Will you dance with me, Dr E? Empowering early childhood practitioners through dance
Bibliographic record
Abstract
Dance education in preschool has been linked to many benefits for children’s development. However, research shows that practitioners often lack the confidence to implement such activities, as they feel they are missing substantial knowledge in this area. This paper aims to address this issue and empower practitioners to use dance as an essential tool in their teaching practice. The project did not aim to teach specific dance styles but to support practitioners to use dance as a collaborative process with children, giving them a voice. The intervention took place in four preschool classrooms over four months in South-East London. 18 practitioners participated, using the Dancing with Dr E framework on a weekly basis. Practitioners had no previous experience in dance. The intervention took place for 20–30 mins, three times per week but this was flexible. The outcomes of the intervention were measured with a short questionnaire and semi-structured interviews. Findings demonstrated the benefits of the dance intervention for practitioners’ well-being and confidence, providing opportunities for self-reflection, mindfulness, and following a child-led methodology. The sustainability of the project was confirmed, as practitioners planned to integrate it into their future practices in various ways, supporting the community and reaching a wider audience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".