Lifespan models of athlete development: What have we learned from previous attempts?
Bibliographic record
Abstract
Sport has a unique place in many cultures, emphasizing the links between physical elements of movement with psychological and social outcomes. Sport participation continues to attract the interest of researchers from a range of perspectives, yet there remains a strong need to understand the "who", "what", "where", "when" and "why" aspects of sport involvement over the life course. While the research literature includes multiple athlete development models that consider these components, they are incomplete frameworks for understanding lifespan sport engagement. In this article, we discuss the value in building multidimensional developmental models of sport participation that encapsulate experiences across all ages and stages of competitive and recreational sport, and pay special attention to the high degree of complexity of the movement between and within sport both competitively and recreationally. In addition, we highlight several challenges to creating such a lifespan development model, and consider areas of future direction to overcome some of these hurdles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".