Bibliographic record
Abstract
Background and purpose: The research literature in physical education (PE) is placing a growing emphasis on Meaningful PE (Beni et al, 2017) to transform PE to meet the needs of all students. The purpose of this research was to 1) identify the concepts of Meaningful PE that students found to be important and 2) distinguish which concepts have the most potential to provide students with Meaningful PE experiences. The study: The project was conducted in three PE classes among grade 7 to 9 students in an urban secondary sports academy school in collaboration with their PE teachers. Data was collected using the GroupWisdomⓇ Concept Mapping (2021) platform and group interviews with the objective to have PE students and teachers conceptualize Meaningful PE. Findings: The study found that students’ and teachers’ context specific conceptualizations of Meaningful PE can be identified using GCM. The major tenet of Meaningful PE found was relationships. Student and teacher participants identified important concepts for Meaningful PE as a combination of statements within the clusters of kindness, physical activity, fun, and quality education. The findings call for a broad understanding of students within each school context in order to conceptualize meaningful physical education experiences. Conclusions: It is my conclusion that involving students in the conceptualization of Meaningful PE by focusing on autonomous and inclusive relationships is of great importance to co-create Meaningful PE experiences. Secondary students were able to identify what is important for meaningful experiences in PE and how inclusive relationships can facilitate Meaningful PE experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".