Children's Embodied Voices: Approaching Children's Experiences Through Multi-Modal Interviewing
Bibliographic record
Abstract
This article focuses on a multi-modal interview approach that has been developed as part of a research project. The goal of the research was to explore and better understand children's embodied experiences and expressions in movement. The multi-modal interview approach emphasizes the non-verbal, giving children an opportunity to focus on "the felt sense" (Gendlin, 1983), and to express their experiences in a variety of forms and through the use of metaphors (Egan, 1997; Gendlin, 1983, 1997). Inspired by Arnold Mindell's (1985) work on shifting channels in our ways of experiencing the world, this paper works with an adaptation of Eugene T. Gendlin's "focusing technique" one that significantly expands Gendlin's repertoire of modalities by using drawing, colours, words, sound, music and movement. Narratives have been created using children's voices and expressions. The article includes an example of a narrative that illustrates how the approach has helped children express their movement experiences. The narrative is analysed by means of a hermeneutic phenomenological approach (van Manen, 1990), through which themes/lived meanings of the child's experiences are elucidated. The article closes with a discussion of how the multi-modal interview approach can help to cast light on the relationships between body, movement, and language, and how the approach could also inspire a somatic perspective when teaching movement and dance in schools.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".