Knowing and Understanding how to Manage One’s Physical Activity Practice: Contribution of Language, Thinking and Intelligence to Physical Literacy
Bibliographic record
Abstract
Agreed upon components of physical literacy are (a) physical competence, (b) knowledge and understanding, (c) motivation and confidence, and (d) lifetime engagement. The purpose of this article is to discuss the development and use of the “knowledge and understanding” PL component in older students and adults with regard to the regulation of their health/fitness- and leisure-related physical-activity-practice (PAP). In a first section the author considers the pedagogical content knowledge (PCK) and the basic language that may be associated with the management of health- and fitness-oriented physical activities, differentiating elements that pertain to declarative, procedural or conditional knowledge. Based on exercise-monitoring procedures (E-MP) (essentially procedural knowledge) and on exercise-management rules (E-MR) (mostly conditional knowledge), the following section focuses on the development of PAP-management understanding and the related intelligence in its analytical, creative and practical dimensions. In a final section, the author explores briefly the matter of awareness and regulation in terms of exercise-management knowledge and understanding. Keywords: exercise-management awareness, exercise-management regulation, FITT formula, physical-activity monitoring
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".