Clarifying the Concept of Communities of Practice in Sport
Bibliographic record
Abstract
In an attempt to describe learning outside of the usual official curriculum, concepts such as workplace learning, nonformal learning, informal learning, and incidental learning have been used in the fields of teacher education, workplace pedagogy, and sport. These ‘outside-of-the-classroom’ learning opportunities are characterized by the important role that peers play in the learning process. Participation in communities of practice (CoPs) provides one such opportunity for learning. Recent interest in this concept for coach education and in sport has resulted in an increasing number of studies in which researchers promote CoPs, but not all these studies operationalise CoPs clearly and according to Etienne Wenger's framework To clarify this situation and further stimulate the discussion around the potential of CoPs in sport, we present a brief history and description of the concept, compare it with related notions, and describe some recent studies on CoPs in sport.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".