Bibliographic record
Abstract
The purpose of this chapter is to discuss the relatively new, yet emerging issue of who should be teaching children physical education and school sport in primary schools? Traditionally in most western school systems, including countries such as England, America, Australia and Canada, physical education curriculums and programmes of study have been designed, developed, taught and evaluated by qualified teachers (Kirk, 2010). In primary schools this has been the generalist class teacher, professional educators who have not necessarily specialised in physical education (Blair and Capel, 2011 and Sloan, 2010). However, in recent years research has shown that physical education in primary schools has also been taught by people other than the class teacher (Stewart, 2006, Blair and Capel, 2008, 2011; Griggs 2008, 2010, Powell, 2015, Sloan, 2010, Williams, Hay and Macdonald, 2011 and Williams and Macdonald, 2015) typically non-professionals who have coaching qualifications from National Governing Bodies (NGB) and specialise in aspects of youth sport (Blair and Capel, 2008, Griggs, 2010). Extra-curricular school sport has again traditionally been delivered by qualified teachers in some countries for example England, but in others such as America coaches have been used to support and deliver extra -curricular sports teams.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".