Four domains for development for all (4D4D4All): A holistic, physical literacy framework
Bibliographic record
Abstract
People participate in many different types of physical activity, both daily and across their life span, but research on human movement is often siloed by type (sport, exercise, active transportation, etc.). Models developed for understanding participation in specific types of physical activity remain useful in those contexts, but are insufficient for describing, explaining, or enhancing movement at the individual or societal level. Physical literacy has the potential to serve this purpose, but its current application does not fulfill its potential for holistic and inclusive promotion of movement. This paper therefore introduces a new framework for physical literacy, Four Domains for Development for All (4D4D4All). This framework highlights the interrelations among the physical, psychological, social, and creative aspects of movement, and emphasizes how development across all four domains can be supported through the interrelations of individuals and their contexts. In this paper, we introduce the 4D4D4All framework and present ideas for its application in research and practice. Specifically, this individualized, holistic, and inclusive approach to physical literacy is an alternative to models that emphasize the production of podium-driven athletes or focus on meeting physical activity guidelines, and is therefore better suited to facilitate the development of flourishing active people.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".