Pushing your luck: on chance, serendipity, and athlete development
Bibliographic record
Abstract
The notion that problems with prediction can be resolved with more, and better, data has a long history. In this paper, we examine the role of chance and randomness (i.e., events where there is a low probability of occurrence) in athlete development, focusing on the influence of 'luck' on this process. More specifically, we briefly summarize the way luck has been considered in previous research on human achievement and how different types of luck (i.e., luck related to elements of the task, the athlete development environment, and biological processes) can affect athlete development. In addition, the implications and challenges of embracing the influence of luck on models of athlete development are discussed. Acknowledging the role of luck may lead to developmental environments that are more equitable (e.g., by creating greater opportunities for more individuals to get lucky) and realistic (i.e., by acknowledging that predictions of sport- and athlete-related outcomes will never be perfect).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".