Coach Selections and the Relative Age Effect in Male Youth Ice Hockey
Bibliographic record
Abstract
UNLABELLED: Relative age effects (RAEs; when relatively older children possess participation and performance advantages over relatively younger children) are frequent in male team sports. One possible explanation is that coaches select players based on physical attributes, which are more likely witnessed in relatively older athletes. PURPOSE: To determine if coach selections are responsible for RAEs by comparing RAEs in male players who played competitive versus noncompetitive ice hockey. METHODS: Using chi-square, we analyzed the birth dates of 147,991 male ice hockey players who were 5 to 17 years old. Players' birth dates were divided into four quartiles, beginning with January to March, which coincides with Hockey Canada's selection year. RESULTS: There were strong RAEs (p < .001) when players were selected to competitive teams by coaches through a tryout system. On noncompetitive teams that did not have coach selections, there were strong RAEs (p < .001) from 5 to 8 years old, but not 9 to 17 years old. CONCLUSIONS: Although coaches might perpetuate RAEs, other influential social agents might include parents, which ought to be investigated in future research.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".