When I Dance My Walk: A Phenomenological Analysis of Habitual Movement in Dance Practices
Bibliographic record
Abstract
In this article, I describe the experience of dancing-a-walk. My specific focus is on the shift that I perceive in my body when I dance-a-walk rather than functionally walking. Following a firstperson perspective, I demonstrate how my experience of practicing dancing-a-walk interrogates the habit of walking and makes it come alive again as an expression of the body. First, I show how the practice of dancing-a-walk challenges the dichotomy between abstract and concrete movement proposed by Maurice Merleau-Ponty in the Phenomenology of Perception. Indeed, dancing-a-walk is an example of a concrete and yet already abstract movement. Then, I turn to concepts such as habits and body memory. By identifying how the perception of my body changes when I dance everyday movements (i.e., walking) versus when I execute such movements functionally, I aim to develop a new perspective on and vocabulary for a phenomenological definition of concrete/abstract movements within the context of dance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".