THE POWER OF UNITED STATES IN DOMINATING MIXED MARTIAL ARTS SPORTS THROUGH ULTIMATE FIGHTING CHAMPIONSHIP
Bibliographic record
Abstract
The United States of America (USA) dominates in almost every aspect globally, including the sport of mixed martial arts (MMA). Most of the MMA sports promotion companies that dominate the global market are from the US. One of the largest and most popular is the UFC. As a global company, UFC is the most dominant MMA sports competition promoter in the world today due to its global economies of scale. Since its inception in the US, the UFC has developed into competitions held in several countries, such as Canada, the UK, Russia, Brazil, Uruguay, the United Arab Emirates, Australia, New Zealand, South Korea, and Singapore. This research aims to find out the UFC’s business strategy as a global company in globalizing its commodities to dominate the mixed martial arts (MMA) sports industry on a global scale. This research uses a descriptive qualitative method accompanied by a 4P Marketing Mix perspective as an analytical tool in examining the strategies implemented by the UFC. This research concludes that the UFC’s global marketing strategy is to implement various marketing elements from upstream to downstream optimally so as to be able to dominate the market share of the global MMA sports industry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".