Bibliographic record
Abstract
The field of physical activity (and related) health) education (“poor, old ‘PE’”) needs to assert its will to win more vigorously then ever before. Scholarly and scientific investigation of the past 60 years since Sputnik was launched in 1957 has identified a wide variety of findings proving that a quality program can provide highly important benefits to the growing child and youth. Societal developments, including other curricular demands, have undoubtedly created uneasiness within the overall field of education. In North America the time and attention devoted to the relatively few involved in external highly competitive sport for the few has been a negative factor. At the same time intramural athletics for the large majority of children and youth has not been available to the extent it should be. There is now doubt as to the field’s ability to achieve high status within education. Therefore, we must pledge ourselves to make still greater efforts to become vibrant and stirring through absolute dedication and commitment in our professional endeavors. Ours is a high calling since we seek to improve the quality of life for all people on earth through the finest type of human motor performance in exercise, sport, and related expressive movement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".