Bibliographic record
Abstract
This paper will explore how game-play in video games as well as game centered approaches in physical education (PE) such as Teaching Games for Understanding (TGfU) can draw on complexity thinking to inform the learning process in physical education. Using the video game concept of game-as-teacher (Gee, 2007), ideas such as enabling constraints from complexity thinking (Davis & Sumara, 2006) and information-movement couplings from motor learning (Davids, Button, & Bennett, 2008), learning will be framed as emergent, adaptive and self-organizing. To explain these concepts the following examples will be used (1) an auto-ethnographic narrative of the author's memories learning to play tennis with his father, (2) an account of a beginner learning to play as an avatar in the video game Guild Wars, and (3) a group of beginners learning to play tennis using a TGfU approach. Drawing on the author's narratives and the video game concept of game-as-teacher, the paper concludes by emphasizing the principle of modification by adaption as a way to engage players of different abilities to experience worthwhile game-play in PE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".