Bibliographic record
Abstract
To accurately grasp the forefront hotspots and developmental trends in current research on youth sports literacy, we employed the “Web of Science” core collection database to gather 432 relevant literature pieces pertaining to “youth sports literacy”. Using the CiteSpace analysis software and leveraging methodologies such as scientific knowledge mapping, we systematically reviewed and synthesized the literature, ultimately constructing a knowledge structure map that illustrates the research hotspots and cutting-edge trends. The findings of this study reveal that research on youth sports literacy is predominantly concentrated in developed countries and regions such as the United States, Australia, and Canada. The definition and conceptualization of sports literacy represent the fundamental research questions. Research hotspots are primarily clustered around three major themes: Youth sports literacy in relation to public health, physical education, and mental well-being. The emphasis on research frontiers varies across different periods: The period from 2007 to 2012 reflects a focus on psychological promotion; 2013 to 2018 emphasizes holistic physical and mental well-being promotion; and 2019 to 2022 represents a phase centered around health promotion. Overall, a trend towards integrated research in youth sports literacy is evident. The wealth of knowledge clusters and impactful research achievements have laid a robust foundation for the study of youth sports literacy.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".