Leaning into life with somatic sensitivity: Lessons learned from world-class experts of partnered practices
Bibliographic record
Abstract
Partnered practices reveal somatic insights into leaning-in and prompt us to consider how we can move responsively and interactively with others. Particular experiences of relational leaning are described through a motion-sensing phenomenological approach framed by the authors’ Interactive Function2Flow model of somatic education. With sensitivity to movement function, form, feeling and flow, this relational leaning is explored through the slow and controlled balances of acroyoga, the gentle forward and backwards lunges of push hands tai chi, the fast paced, rhythmical walking of salsa dance, and the effervescent gait transitions of equestrian arts. We consider the act of leaning-in and the relational awareness of each partnered practice in terms of the life lessons of connecting with a partner, responding to conflict with composure, giving less or more of oneself in a given situation and, in so doing, moving with enhanced motion-sensitivity into a state of interactive flow.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".