Designing Experiential Learning Curricula to Develop Future Sport Leaders
Bibliographic record
Abstract
As sport management graduates enter into a rapidly shifting industry with fluctuating environmental conditions, the need for greater leadership capacity arises (Amis, Slack, & Hinings, 2004). Sport management educators can facilitate leadership development by designing and administering undergraduate curricula that focuses on students’ vertical development. According to Cook-Greuter (2004), vertical development is defined as “how we change our interpretations of experience and how we transform our views of reality” (p. 276). The purpose of this paper is to outline a curricular framework that may impact students’ vertical development and thus increase future leadership capacity. To fulfill this purpose, the conceptual connection between vertical development, the Leadership Development Framework (LDF), and Experiential Learning (EL) is first explained. The curricular framework is then outlined in the context of a pilot study facilitated within a sport management (leadership) course in January 2008. Suggestions for future empirical projects to measure the impact of EL curricula on students’ vertical development are also offered.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".