A proposed theoretical model for RAEs in sport
Bibliographic record
Abstract
Relative age, the differences in age between peers within a cohort, has been found to influence a variety of developmental outcomes (i.e., relative age effects). In sport, relatively older youth typically experience advantages, such as a greater likelihood of being selected to sports teams. Relative age effects have been repeatedly identified in a variety of sports, in athlete samples internationally, and in samples ranging from recreational youth participants to elite adult. However, this research, while important, has been described as somewhat atheoretical (Cobley et al., 2009). As such, we propose a theoretical framework that is evidenced-based and consistent with findings in this area. The foundation of this theoretical model is derived from Newell’s model of constraints (Newell, 1986) and the proposition that outcomes emerge from the interaction between individual constraints (related to the performer), task constraints (related to the demands and rules of the activity) and environmental constraints (related to physical and socio-cultural environmental characteristics). Our model also incorporates an epidemiological ‘causal pie’ structure (Rothman, 2002) wherein the specific salient constraints are depicted so that their size (‘pie piece’) is explicitly related to the magnitude of their influence on a RAE. This presentation will outline the evidence that supports the proposed model, and the model’s functional characteristics. In addition, we also present two methodological implications for future research that emerge from the proposed model: macro and micro studies of RAEs. Macro approaches are those that incorporate as broad of a systems-perspective as possible with a multivariate analyses of constraints (individual, task and environmental). Alternatively, micro approaches could test, perhaps even experimentally, the influence and mechanisms of specific constraints. Both approaches may have important implications for advancing this field and for future RAE interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".