On the self-regulation of sport practice: Moving the narrative from theory and assessment toward practice
Bibliographic record
Abstract
This paper reviews theoretical developments specific to applied research around the “psychology of practice” in skill acquisition settings, which we argue is under-considered in applied sport psychology. Centered upon the Self-Regulation of Sport Practice Survey (SRSP) , we explain how self-regulated learning conceptually underpins this survey and review recent data supporting its empirical validation for gauging athletes’ psychological processes in relation to sport practice. This paper alternates between a review of applied research on self-regulated sport practice and new data analyses to: (a) show how scores on the SRSP combine to determine an expert practice advantage and (b) illustrate the large scope of self-organized or athlete-led time to which SRSP processes may apply. At this stage, the SRSP has been established as a reliable and valid tool in the empirical, theoretical domain. In order to move the narrative from theory and assessment toward applied practice, we present evidence to propose that it has relevance as a dialogue tool for fostering meaningful discussions between athletes and sport psychology consultants. We review initial case study insights on how the SRSP could be located in consultation in professional practice, propose initial considerations for its practical use and invite practitioners to examine its utility in applied settings.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".