A commentary: Aspiring athletes’ transition literacy and beyond
Bibliographic record
Abstract
In this commentary we take a scientist practitioner’s perspective to briefly overview major approaches in career transition research and interventions, summarize a quintessence of athletes’ career transition knowledge in the form of transition literacy postulates, and proceed with how transition literacy can become a resource for athletes, coaches, parents, sport psychology and allied professionals seeking to facilitate athletes’ pursuits of career excellence. We suggest considering transition literacy as (a) the basic transition competencies derived from related theories and research and (b) an important part of athletes’ and their supporters’ broad-based expertise, in addition to more commonly considered performance related knowledge and skills. Distilled from major review papers on athletes’ career development and transitions, the transition literacy postulates also termed the “golden rules” of transitions address the roles of transitions in athlete career development, transition processes, phases, pathways and outcomes, as well athletes’ environments and transition support. We conclude with recommendations of how transition literacy can be a useful part in athletes’, parents’ and coaches’ education and thereafter, provide recommendations for the education of sport psychology professionals.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.051 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".