Building Tomorrow’s Sports Fans: Strategic Initiatives in Youth Engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
National Hockey League fans in the United States are aging, presenting a challenge for the League’s future growth. The Toronto Maple Leafs (the Leafs), the National Hockey League’s most valuable franchise, recognize that younger generations engage with sports differently than traditional fans and often perceive hockey as less appealing compared to other major sports. Despite their loyal and historic fan base, the Leafs understand that sustaining long-term growth and cultural relevance requires attracting and engaging younger audiences. To address this, the Leafs launched the Next Gen initiative, transforming select home games into youth-focused experiences and leveraging digital strategies tailored to Gen Z. This case presents a critical question: How can National Hockey League teams—and other sports leagues and teams more broadly—build sustainable, younger fan bases essential for their future? Through this case study, students will analyze demographic trends in sports fandom, identify the challenges teams face in expanding their fan base, evaluate targeted marketing strategies, and propose innovative solutions to help marketing managers effectively engage and retain younger audiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it