Do participant reporting practices in youth sport research adequately represent variability in sport contexts
Bibliographic record
Abstract
A significant portion of research-oriented toward exploring the developmental benefits attained through has been informed by social-ecological approaches (e.g., Holt, 2017). Accordingly, athlete development researchers must consider the structure of the activities, interactions with social agents, and appropriateness of the settings—not only as descriptive features but as aspects that influence outcomes (e.g., Cote et al., 2015). Considering that athlete development does not occur in a vacuum and that a range of contextual factors must be considered, this study sought to (1) demonstrate the contextual variability of youth sport within a mid-sized Canadian city and (2) explore participant reporting practices within a subset of journals within psychology. Phase 1 involved a scan to identify opportunities within the city and surrounding area. During Phase 2, researchers systematically reviewed articles involving athletes (aged 6-18) published within four peer-reviewed journals between 2000 and 2017. Phase 1 demonstrated that opportunities varied greatly pertaining to type, gender, level of competition, cost, and time commitments. The review of reporting practices in Phase 2 highlighted a tendency to list basic demographic information like gender, age, and sample size, while using vague terms to denote context (e.g., competitive level) instead of the actual elements of contexts (e.g., number of hours, tenure, ethnicity, cost). Accordingly, researchers might consider more specifically describing the contexts that they are studying, while also speaking to the role that those contexts have pertaining to athlete experiences (e.g., other or extracurricular involvement).Acknowledgments: This research was partially funded by the Social Sciences and Humanities Research Council of Canada, Canada Graduate Scholarship for Master's Students.
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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 | 0.208 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".