Putting Research to Work in Elementary Physical Education: Conversations in the Gym
Bibliographic record
Abstract
Part 1 The basics - numbers, time, space, equipment and behaviors: how equipment and class size make a difference who gets the teacher's attention? more days per week equals more learning modifying equipment to fit the students. Part 2 Managing the class: reducing disruptive student behaviors active supervision and student learning teaching social behaviors recording and classifying teacher management behaviors teaching fair play in the gym and school. Part 3 Interactions among students: using trained peer tutors peer-mediated accountability and successful practice. Part 4 Strategies for teaching and learning: can learners guide their own practice with self-talk? using environmental cues to guide practice. Part 5 The voices of students: kindergarten children describe physical education third-grade children describe physical education children describe the mile-run fitness test children describe what they learn in physical education high school seniors recall elementary physical education. Part 6 Teachers in the workplace - training, experience and context: preservice classroom teachers in the gym is daily physical education worth the price? the impact of inclusion on physical education teachers how five years of experience changed a teacher how veterans and rookies planned a class. Part 7 Assessment as part of training: using skill assessment to build effective lessons is peer assessment both practical and accurate? Part 8 The SPARK studies - a program for teachers and children: the impact of SPARK on children's fitness and activity the impact of SPARK on catching, throwing and kicking how well do children like various physical education activities?. Part 9 Reviewing studies of the effects of physical education: the legacy of elementary school physical education. Part 10 The Trois-Rivieres studies - long-term effects of physical education: twenty-year follow-ups of participants in a model program.
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 | 0.029 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".