Symposium overview: Envisioning the future of positive youth development research in sport
Bibliographic record
Abstract
Research in sport situated under the broad umbrella of positive youth development (PYD) has blossomed in the past decade, leading to a vibrant and diverse body of work. This expansion has led to a number of empirical and theoretical contributions and has positioned sport as a valuable and unique context for PYD research. As with any burgeoning area of research though, we run the risk of re-inventing the wheel if we do not take the time to pause and appraise our strengths and weaknesses, in order to identify key areas of importance for the future of PYD research. As such, it is timely to reflect upon the current state of PYD research in sport, its contributions to the broader field of PYD research, and to pose critical questions for researchers to tackle as we move forward in the area. Two significant issues that will be considered in this symposium are: (a) what might we learn about PYD from research in various settings beyond traditional competitive sport contexts? and (b) what role do theories play in shaping the field of PYD research? This symposium brings together theoretical and empirical presentations by established and early career researchers to examine 'where we are' and 'where we might go' in the field of PYD research in sport.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".