Heightening Leader Development for Student Staff in College and University Recreational Sports Departments
Bibliographic record
Abstract
University officials frequently trumpet their institution's record of developing leaders and many point to co-curricular programs (e.g., athletics, student government) as ideal settings for this type of development.Researchers have determined that interuniversity athletics programs are potentially fertile grounds for leadership development.The authors believe that recreational sport programs are undervalued and often underutilized units for the leader development in students.They suggest that collegiate recreation programs also offer vast opportunities for leadership development.High numbers of students occupy senior leadership positions with decision-making responsibilities, and they routinely engage in experiences that have the potential to develop and/or refine their leadership skills.However, this development could be enriched and enhanced.The authors chronicle the latest advancements in both leadership and leader development and offer Recreational Sport professionals 10 recommendations to heighten leadership development in their programs.Implementing these recommendations will facilitate richer learning and deeper development, and better align the programs with the trumpeted aspirations of their institution's senior leaders.The potential benefits to the student participants and the programs are also outlined in the paper.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".