Centralizing an ecological sport psychology through science-practice dialectics
Bibliographic record
Abstract
There has been considerable discussion for more than 50 years of how scientists and practitioners in elite level sport can work collaboratively to ensure that evidence-based practice augments the sport performance and human development of elite amateur and professional athletes. The bridging of these two, often disparate competencies, science and practice, though considered at the conceptual level, continues to be scarcely evidenced within the international sport science community. Much of the research that frames the experiences of elite athletes and their consequent needs, is heavily influenced by scientists, often without direct reciprocity to bridge science, theory, and applied context. The knowledge influencing these interventions has derived from qualitative methods, such as semi-structured interviews, surveys, and focus groups, as well as a breadth of psychometric assessments. Though these approaches to gathering robust data are a necessary part of inquiry, they often produce decontextualized data collection strategies and results, which can lead to generalized, ineffective practices in sport performance environments. Within this submission, the first author cooperated with an international team of scientist-practitioners who are well versed in elite sport to delineate ecologically sound science-practice reciprocity. The authors consider the strengths and weaknesses of conventional qualitative research strategies in terms of their utility and the parlance of evidence into intervention and world-class performance. Two emerging, context driven approaches to inquiry are proposed; arts-based methods and an idiosyncratic approach to ethnography to encourage the reader toward an expanded selection of inquiry approaches from which better understanding and intervention can be generated. This contribution conclude with summary points to open further possibilities for innovative science to practice approaches.
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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 | 0.062 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.014 | 0.169 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.008 | 0.011 |
| 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".