Toward a more critical dialogue for enhancing self-report surveys in sport expertise and deliberate practice research
Bibliographic record
Abstract
Two hallmark criteria are commonly used to determine whether a variable of interest has an impact on sport expertise development: (a) discrimination of performance or skill levels and (b) association with time spent in deliberate practice activities. Our opinion is that there has been warranted criticism of the deliberate practice framework and greater methodological rigour will invigorate survey research in this area. In this paper, we aimed to provide critical perspectives on self-report methods previously used to assess group discrimination and to measure deliberate practice in survey-based work in the context of sport expertise as well as to illustrate steps that could be taken to improve confidence in the validity and reliability of these measures. First, we focus on challenges discriminating between multiple, progressively skilled groups of athletes and outline two strategies: one aimed at improving the validity of skill grouping using standardized performance measures, and another illustrating how researchers can assess variability within skill levels. Second, we highlight challenges in measuring deliberate practice activities and propose a funnel method of narrowing athletes’ estimates from general sport activity to highly individualized, purposeful practice. We argue more attention is needed on the development of self-report methods and measurements to reliably and validly assess sport expertise development.
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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 | 0.885 | 0.898 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.048 | 0.058 |
| Open science | 0.013 | 0.030 |
| Research integrity | 0.026 | 0.054 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".