Bibliographic record
Abstract
relative age effect, ice hockey, talent identification, birthdate effect, categories in sport, age discrimination, personal development Perhaps surprisingly, an individual's date of birth has a notable and long-term influence on their development.Any kindergarten or grade 1 teacher will speak of the remarkable difference, physically and/or psychologically, between the younger and older children in their class.While this influence had been documented in schools since the 1960s (1), it was not until the 1980s that the phenomenon was noted in sport, in ice hockey in particular (2, 3).Curiously enough, in sport, this seemingly-important difference has been long disregarded.However, despite over 40 years of research on this issue, the problems it causes remain unresolved.The effect of an individual's birthdate on sport participation and attainment, eventually referred to as relative age effect (RAE), went from an object of curiosity to a cruel and pervasive reality that is a worldwide and widespread phenomenon (4-6).Consistently, it is a key factor explaining success in sport, and in talent identification selections.It eventually became popular as an example for explaining that success in general, in several spheres of activity, could be related to an arbitrary decision of how and when to group individuals to provide consistency in instruction and training. 1There is also an effect of the moment of birth in baseball, and this effect is important in Japan where the cutoff date is April 1st (7).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".