Developing an Organizational Mission Statement in Youth Sport: Utilizing <i>Mad Libs</i> as a Novel, Shared Leadership Approach
Bibliographic record
Abstract
Sport organizations often utilize mission statements as “road maps” to guide the design and delivery of sport to youth. In the present work, we utilized a novel technique and sought out the perspectives of multiple stakeholders to craft a mission statement for an elite youth volleyball club on the east coast of the United States. Prior to the competitive season, a subset of club administrators (n = 3) head coaches (n = 6), parents (n = 10), and athletes (n = 11) participated in Mad Libs, a phrasal word game in which individuals are asked to fill in missing words in a prescribed, written story template. Key mission-relevant words were left blank, and beneath each blank was a prompt such as “noun (what the club should provide)”, “verb (what the club should do)”, or “adjective (kind of partnerships the club should build).” Participants completed stories individually, and responses were synthesized using content analysis. We then crafted a three-sentence mission statement and shared it with club stakeholders at a preseason meeting. The mission statement was adopted by the club and guides the direction of the club and its members. Importantly, our work highlights a novel technique, informed by a range of stakeholder perceptions and experiences, that can be used to craft an organizational mission statement in elite youth 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".