Sport Analytics Education for Future Executives, Managers, and Nontechnical Personnel
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
Successfully adopting sport business analytics to enhance organization-wide business processes necessitates a combination of business acumen, modeling expertise, personnel coordination, and organizational support. Although the development of technical skills has been well mapped in analytics curricula, informing future leadership and affiliated nontechnical personnel about the sport business analytics process, specifically, remains a gap in sport management curricula. This acknowledgment should compel sport management programs to explore strategies for sport analytics training geared toward this population. Guided by experiential learning and foundational business analytics frameworks, a seven-module approach to teaching sport business analytics in sport management is advanced with a particular focus for future executives, managers, and nontechnical users in the sport industry. Concomitantly, the approach presents learning goals and outcomes, sources for instructors to review and consider, and sample assessments designed to fit within the existing sport management curricula.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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