An examination of relative age and athlete dropout in female developmental soccer
Bibliographic record
Abstract
Historically, the relative age effect (RAE) was thought to be driven by level of competition and talent identification processes. More recent investigations show the effect is present at early levels of competition. Given the presence of the RAE at introductory levels, it is necessary to evaluate dropout from sport across competition levels as the development of expertise is predicated by ongoing participation. The objective of this study was to examine dropout in a female cohort retrospectively across seven years (i.e., pre-adolescent to post-adolescent transition years), with respect to relative age and level of competition (i.e., competitive versus recreational). A chi-square analysis was conducted to ascertain whether a RAE was present in the initial year of registration entries; followed by a survival analysis to assess the impact of relative age on dropout from female developmental soccer in Ontario (n = 9,908). An over-representation of players born in the second quartile was observed in the initial year (age 10 years). Preliminary findings suggested relatively older players were statistically more likely to remain engaged in soccer (p < .001) over the seven-year period, however the trend was not practically significant (w = 0.05). When competition level was considered, the cumulative survival for recreational and competitive level players was 20.7% and 55.9%, respectively; indicating a greater rate of decline at the recreational level. This suggests that participation trends may depend on sport context and further analysis is warranted.Acknowledgments: This research was supported through a Social Sciences and Humanities Research Council Doctoral Fellowship (K. Smith).
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".