Game Plan Disrupted: Strategic Planning Missteps in Community Soccer
Bibliographic record
Abstract
Harmony Soccer Club, a nonprofit youth soccer club in Summit Ridge, Ontario, has faced declining membership and organizational challenges since 2016, exacerbated by the COVID-19 pandemic. Sofia Kim, the Executive Director, had only joined the club 2 months prior and was just beginning to get comfortable in her new role. Now, Sofia must make critical recommendations after the club’s recent Annual General Meeting revealed significant discontent with a newly approved strategic plan. Members felt that the strategic plan did not adequately reflect their needs or involve sufficient stakeholder engagement. This discontent resulted in the ousting of the Board of Directors (board) and the election of a new president and directors at the Annual General Meeting. Sofia is asked to lead a comprehensive review of the strategic planning process. Imagining themselves in the role of Sofia, students will examine the implications of the board’s turnover, the failures in the strategic planning process, and the importance of incorporating member feedback. The case underscores the need for a strategic plan that resonates with diverse member perspectives to ensure the club’s future stability and success. This case study is appropriate for both upper level undergraduate and graduate sport management courses, where topics discussed include strategic management, organizational theory, and/or governance.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".