Becoming InterActive for Life: Mobilizing Relational Knowledge for Physical Educators
Bibliographic record
Abstract
The overarching purpose of the InterActive for Life (IA4L) project is to mobilize relational knowledge of partnered movement practices for physical education practitioners. Through a participatory, motion-sensing phenomenological methodology, relational knowledge gleaned from world class experts in salsa dance, equestrian arts, push hands Tai Chi and acroyoga, and analyzed through the Function2Flow conceptual model, was shared with Physical Education Teacher Education (PETE) students. They, in turn, made sense of the ways these experts cultivate relational connections through a process of designing interactive games suitable for physical education curricula. The kinetic, kinesthetic, affective and energetic dynamics of these games were then shared through professional development workshops, mentoring, and open-access resources. Each phase of the IA4L project invites us to depart from the predominance of individualistic ways of conceiving and teaching movement and instead explore what it means to be attuned to the pulse of life as we break away from tendencies to objectify movement as something our bodies do or that is done to them. Consideration is given to the ways in which meaningful relational connections are formed in and through movement and how this learning prioritizes the InterActive Functions, Forms, Feelings and Flows of moving purposefully, playfully and expressively with others. In so doing, what this research offers is an understanding of how knowledge of an essentially motion-sensitive kind, which can breathe life into physical education curricula, can be actively and interactively mobilized.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".