Maintaining and managing athletic identity among elite athletes
Bibliographic record
Abstract
Researchers have studied athletic identity (AI) and explored the impact that having a strong AI can have on an athlete. Additionally, researchers have explored the maintenance of AI, but only among very specific athletic populations. Little is known about how different athletes manage their AI at various stages in their career (i.e., still competing versus retired). Therefore, the purpose of this study was to explore how both retired and non-retired elite athletes from a range of sports maintain and perpetuate their AI. Five male and eight female elite athletes were individually interviewed on two separate occasions. Participants were asked questions regarding their AI, their successes and failures in sport, and how their feelings of self-worth were related to their athletic achievements. An inductive data analysis process was used, and relevant themes were identified. It was also found that there are things athletes do, or that occur in their environment that maintain and perpetuate their AI. The current findings expand this body of literature by exploring the various strategies elite athletes use to support their AI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".