Talent identification and development in judo: A systematic review
Bibliographic record
Abstract
This review synthesizes the existing literature about talent identification and development in judo and provides evidence-based suggestions to help researchers and practitioners in this area. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant studies (n = 45). The mean quality of the evidence was 94.0%. Most of the studies were published between the years 2014 and 2021 with cross-sectional designs and group comparisons or performance prediction. Studies used batteries of tests focused on expert or advanced samples and measured individual constraints. Few studies examined female samples, psychological skills or biological maturation. Only 20% of the studies used multivariate analyses. On closer examination, there was a high degree of variability in the indicators that were found to discriminate between skilled and less-skilled judo athletes, predict performance and/or predict career pathway. Research in talent identification and development in judo has generally focused on individual constraints related to anthropometric and physiological characteristics, and technical skills in cross-sectional designs. Very little is known about what talent indicators discriminate high skilled judo athletes or predict actual performance or future success. Future research should adopt multidimensional and longitudinal approaches that integrate existing findings about the maturational, psychological and environmental aspects of judo for tracking the most talented judo athletes, especially in female samples.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".