Key Elements of Sports Marketing Activities for Sports Events
Bibliographic record
Abstract
Sports marketing activities comprise people, activities, business and organisation in producing, facilitating, promoting or organising any product (as goods, services and events) for a demand of sports supporters. This theoretical paper aims to introduce and discuss the sports scheme, sports marketing mix, and sports supporters as three key elements which the sports teams need to focus on to implement sports marketing activities in sports events. By and large, sports teams have been implemented marketing principles as well as sponsorships to qualify a sports events as experience and entertainment focus on the supporters (as customers). Sports scheme refers to actors’ network, marketing tools represents the tools to plan and perform marketing activities and supporters are those who support and purchase club goods. Thus, all of them are key relevant elements to organise a sports event (as a game or match). The professionalism of the sports events has required use the sports and non-sports stakeholders' skill to help sports teams to design and provide a sports experience and amusement by means sports marketing tools to format a suitable product and service to a supporter’s audience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".