Beyond Utterances: Embodied Creativity and Compliance in Dance and Dementia
Bibliographic record
Abstract
Practices of creativity and compliance intersect in interaction when directing local dances remotely for people living with dementia and their carers in institutional settings. This ethnomethodological study focused on how artistic mechanisms are understood and structured by participants in response to on-screen instruction. Video data were collected from two long-term care facilities in Canada and Finland in a pilot study of a dance program that extended internationally from Canada to Finland at the onset of COVID-19. Fourteen hours of video data were analyzed using multimodal conversation analysis of initiation–response sequences. In this paper, we identify how creative instructed actions are produced in compliance with multimodal directives in interaction when mediated by technology and facilitated by copresent facilitators. We provide examples of how participants’ variably compliant responses in relation to dance instruction, from following a lead to coordinating with others, produce different creative actions from embellishing to improvising. Our findings suggest that cocreativity may be realized at intersections of compliance and creativity toward reciprocity. This research contributes to interdisciplinary discussions about the potential of arts-based practices in social inclusion, health, and well-being by studying how dance instruction is understood and realized remotely and in copresence in embodied instructed action and interaction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".