Part 1—USTA and Tennis Canada Learning to Play Tennis Initiatives: Applying Ecological Dynamics, Enactivism, and Participatory Sense-Making
Bibliographic record
Abstract
In this series of two articles, we connect complexity theoretical frameworks of ecological dynamics and enactivism to initiatives for learning to play tennis advocated by USTA and Tennis Canada. These initiatives were inspired by the International Tennis Federation commitment to reduce the complexity of learning tennis by rescaling the game for children and novice players. This article suggests that tennis teaching is shifting from a skill and drill approach to one embracing a play-practice-play idea in line with Ecological Dynamics. Considering players as dynamic and adaptive sense-making beings, this article outlines how these initiatives create the conditions to embrace insights from motor learning in relation to a constraints-led approach, and enactivism from embodied cognition. This first article concludes with applying an enactivist teaching strategy called modification by adaptation to these tennis initiatives, showing how players of diverse ability can challenge each other, promoting more game-based dynamic learning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".