Influence of population size, density, and proximity to talent clubs on the likelihood of becoming elite youth athlete
Bibliographic record
Abstract
Previous studies have found significant differences in the likelihood of becoming an elite athlete depending on community population sizes and densities, an effect known as the place of early development, or birthplace effect. However, the results have not been consistent between sports or European countries. As both professional and voluntary clubs are vital to the talent development systems in Europe, the proximity of an athlete's place of early development to the location of talent clubs may be an important predictor of the likelihood of becoming an elite athlete. Therefore, the primary purpose of this study was to investigate the place of early development effect and the effect of proximity to talent clubs. The samples included elite youth league athletes (579 football and 311 handball) and national youth athletes (85 football and 80 handball) and a comparison group of 147 221 football and 26 290 handball youth athletes. Odds ratios showed variations in the optimal community size and density across sports. Geospatial analyses of proximity to talent clubs highlighted a trend indicating that most national and elite youth league athletes in both sports had their place of early development in their sport near a talent club. The results suggest that proximity is an important predictor in the development of expertise across sports, but future studies need to clarify if proximity is important in other countries and sports.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".