Youth Sport and COVID-19: Contextual, Methodological, and Practical Considerations
Bibliographic record
Abstract
One of the growing concerns among youth sport stakeholders is how the COVID-19 pandemic will shape youth sport development. Given the novelty and rapidly changing nature of these events, the impacts on development are not yet clear. Thus, to gain a deeper understanding of the impact of this crisis, it is crucial for researchers and practitioners to examine the effects on youth development at different timescales. Although we are seeing immediate changes in the activities, social dynamics, and settings that are influencing youth's real-time experiences, questions remain regarding its influence on short-, and long-term developmental outcomes (Kelly et al., 2020). Moving forward, we will need to be cognizant of how this watershed moment will shape youth sport development for months and years to come. As such, this opinion article will focus on exploring potential contextual, methodological, and practical considerations that may be relevant as we navigate through these uncertain times. Further, we hope to encourage researchers and practitioners to embrace this as an opportunity to critically reflect and evaluate the existing practice of youth sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.290 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".