‘Athletes’, ‘Talents’, and ‘Players’: Conceptual Distinctions and Considerations for Researchers and Practitioners
Bibliographic record
Abstract
A clearer understanding of, and tighter boundaries between, terms are important for researchers designing studies as well as for other sport stakeholders creating evidence-informed policies. This article considers the terms 'athlete', 'talent', and 'player' from psychological and sociocultural perspectives and in different sporting communities to highlight the importance of terminological clarity in sport research. We present considerations to clarify the use of these terms within different contexts and how the use of specific terms may affect knowledge mobilization in diverse sporting populations. A conceptual discussion is provided to help operationalize development-related terminology and its associated stages, to better reflect contemporary academic thought, and enhance practical interpretations. Importantly, we also call for greater transparency from researchers when presenting findings and encourage practitioners to clearly define key terms when working in sport. Our intention in this paper is to energize readers to consider how we use language in athlete identification and development contexts, to stimulate deeper thought and discourse around the possible implications these terms may have at any point of an individual's development in sport. Greater deliberation, identification, and acknowledgment of the drawbacks accompanying these terms will be needed before more confident assertions can be made on how researchers and practitioners could (or even should) implement certain terminology across youth sport contexts moving forward. This paper adds to a growing literature on the importance of clarity in terminology and acts as an impetus for those working in specific sports to co-design key terms used by researchers, practitioners, and policy makers.
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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 | 0.189 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.014 | 0.140 |
| Scholarly communication | 0.035 | 0.058 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".