The Bibliometric Analysis of Studies on Physical Literacy for a Healthy Life
Bibliographic record
Abstract
This article empirically provides a global overview of physical literacy, which allows for the understanding of the structure of the epistemic community studying literacy for healthy living. Publications registered in the Web of Science are analyzed using bibliometrics (spatial, productive, and relational) based on data from 391 records, published between 2007 and April 2022, applying five bibliometric laws and using VOSviewer software for data and metadata processing and visualization. In terms of results, we observe an exponential increase in scientific production in the last decade, with a concentration of scientific discussion on physical literacy in seven journals; a production distributed in 46 countries situated on the five continents, but concentrated in Canada and the United States; co-authored research networks composed of 1256 researchers but with a production concentrated of around 2% of these, and an even smaller number of authors with high production and high impact. Finally, there are four thematic blocks that, although interacting, constitute three specific knowledge production communities that have been delineated over time in relation to health and quality of life, fitness and physical competence, education, and fundamental movement skills.
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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.201 | 0.279 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| 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".