The Future Is Now: Preparing Sport Management Graduates in Times of Disruption and Change
Bibliographic record
Abstract
COVID-19 disrupted the world, and the impacts have been experienced in many areas, including sport and higher education. Sport management academicians need to reflect on the past two years' experience, determine what worked and what did not work, and avoid the temptation of automatically returning to past practices. The authors of this manuscript applied the disruption literature and propose transformative changes in what sport management academicians teach (e.g., greater emphasis on innovation, entrepreneurship, automation, critical thinking skills to facilitate working in flexible environments and across areas), how colleagues teach (e.g., heightened integration of technology, blended learning models) and where colleagues teach (on-campus and distal delivery modes, asynchronous and synchronous delivery to students on campus and across regions/countries). Examples of start-up companies and entrepreneurial ventures are offered to help illustrate the changing sports landscape and the emerging opportunities for current and future students, graduates, and professors. Sport management professors are offered some suggestions to assist them in seizing this opportunity.
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.
How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".