Circumstantial development and athletic excellence: The role of date of birth and birthplace
Bibliographic record
Abstract
Abstract Researchers are only beginning to understand how contextual variables such as date of birth and birthplace affect the development of elite athletes. This article considers the generality of birthplace and date‐of‐birth effects in varying sport contexts. The Study 1 examined how environmental factors associated with an athlete's date‐of‐birth and size of birthplace predict the likelihood of becoming an Olympic athlete in Canada, the United States of America, Germany, and the United Kingdom. Study 2 examined date‐of‐birth and birthplace effects among athletes playing in the first professional league in Germany. Study 2 also examined the validity of birthplace as a proxy for early developmental environment by comparing birthplace with the place of first sports club in four German sports leagues. Results from both studies showed no consistent findings for date of birth. Findings from Study 2 also suggested incongruence between birthplace and location of first sports club as proxies for early developmental environment. Although there was some consistency suggesting elite athletes are less likely to come from very small or excessively large communities, exceptions occurred both within and across sport contexts. These results suggest that any developmental effects of date and place of birth are buffered by broader socio‐cultural factors.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".