Physical education teachers’ (lack of) gymnastics instruction: an exploration of a neglected curriculum requirement
Bibliographic record
Abstract
Gymnastics is named as one of four or five broad movement domains within all of Canada’s provincial/territorial physical education (PE) curriculums. However, in practice gymnastics is afforded less relative instructional time than are all other movement domains. In light of this observation, we researched Atlantic Canadian PE teachers’ gymnastics instruction, aiming to answer three primary research questions: (1) Why does gymnastics occupy such a limited (relative) amount of instructional time?; (2) What value do PE teachers see in teaching gymnastics?; and (3) How can PE teachers develop the requisite competence and confidence to offer more gymnastics instruction in their PE programs? Employing a series of on-line focus group interviews with purposefully selected PE teachers, our results indicated that participants did indeed value gymnastics. Participants also believed the lack of gymnastics instruction was due to PE teachers’ strong focus on other areas/topics, as well as their lack of competence and confidence in teaching gymnastics. Herein, we offer a summary discussion of these results as well as suggestions for future practice and inquiry. The results and discussion should be of interest to those who share an interest in PE, curriculum inquiry, and gymnastics instruction, particularly within Western schooling contexts.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".