Towards an ecology of athletes’ career transitions: conceptualization and working models
Bibliographic record
Abstract
In the present paper, we introduce an ecological view of career transitions. We consider an athlete’s development as a journey through various athletic and non-athletic environments that support their striving for career excellence. On this journey, an athlete experiences a multitude of environments and transitions from one environment (e.g., one club, one country or one sport) to another. To develop this understanding, we introduce the concept of a transition environment defined as a dynamic and temporary system that bridges the setting that an athlete is transitioning from and to. We also suggest two working models that in unison can work as a roadmap for transition environment research and practice. The transition environment (TE) model helps to describe the TE and the transition environment success factor (TE-SF) model helps to understand why certain TEs are more successful than others supporting athletes in transition. The models can be used by researchers studying specific transition environments to understand how such environments facilitate or hinder transitions, and by practitioners (coaches, managers, sport psychologists) to support athletes’ transitions by improving their TEs. We hope the idea of an ecology of athlete transitions will find its way into empirical studies of different types of transitions (e.g., to another sport, to another level in sport, to another club or to another country) in multiple cultural contexts and contribute to the development of career-long psychological support services.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".