Practice and Implications of Emerging Technology on Sport Management
Bibliographic record
Abstract
Sport has always been under pressure to change. Fortunately, we can see the pressures coming as emerging technologies are published online. This situation offers an advantage as sport management students can be taught to ponder such technological advancements. No one has the complete right answer(s) today - but we can speculate and begin to prepare sport for the emerging technologies. This paper outlines five (5) advancing technologies and proposes questions for debate on their potential impact on sport. The technologies include: sporting equipment and 4D printing; deep brain stimulation and competition anxiety; block chain management; human driven drones and long distance races; and preparing for races that are higher, faster, and further …. around the moon and back. It is important to begin to prepare so we ‘get it right’ as an example of potentially not getting it right is offered to start the discussion. Insights and debate can aid to devise strategies concerning the way forward in emerging times. This means we have an opportunity to contribute to leading edge education and advance management skills for the future of sport. Significant change is happening – and sport management educators can aid in getting ahead of the issues. Keywords: sport management education, emerging technologies and sport, management of sport challenges, sport policy
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".