Integrating Citizenship, Embodiment, and Relationality: Towards a Reconceptualization of Dance and Dementia in Long-Term Care
Bibliographic record
Abstract
Dance, as aesthetic self-expression, is a unique arts-based program that combines the physical benefits of exercise with psychosocial therapeutic benefits. While dance has also been shown to support empowerment, meaningful self-expression, and pleasurable experience, it is rarely adopted to support these aspects of engagement in the context of dementia care. The instrumental reduction of dance to its application as a therapeutic tool can be traced to the contemporary movement towards cognitive science with an emphasis on embodied cognition. This has effectively elided a consideration of how the body itself, separate and apart from cognition, could be a source of intelligibility, inventiveness, and creativity. We argue for the need to broaden the therapeutic model of dance to more fully support embodied and creative self-expression by persons living with dementia. To achieve this, we explore how a relational model of citizenship that recognizes corporeality and relationality as fundamental to human existence brings a new and critical dimension to understanding the importance of dance in the context of dementia. Drawing on this model, we articulate a new kind of ethic characterized by a pre-reflective intercorporeal sensibility that requires the mobilization of public structures and practices to cultivate a relational environment for individuals living with dementia that supports human flourishing.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".