Bibliographic record
Abstract
Research shows that adolescent girls (aged 12-15 years old) are experiencing a decline in physical activity. Research shows that adolescent girls are more likely to opt out of physical education programming when it is no longer mandated in schools (Landolfi, 2013; Statsistics Canada, 2011). The purpose of this study is to inquire how a sample of intermediate physical educators are eliciting greater participation from female students through formal and informal opportunities in physical activity. The main research question guiding this research project is: How is a sample of intermediate physical education teachers eliciting greater participation from female students in formal and informal opportunities in physical activity? Subsidiary questions include: What do these teachers observe as outcomes from these students’ participation in physical activity? What are these teachers’ perspectives on why adolescent female students may be reluctant to participate in physical activities in school? This research is a qualitative study where three educators were chosen to take part in a 45 min semi-structured interview. The findings from this study are that positive teacher strategies in physical education classes such as: motivation, encouragement and modeling, can help increase student engagement, and foster self-efficacy in physical activity.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".