Sports industry research North America: USA & Canada
Bibliographic record
Abstract
The Sports Industry is a potential business that not only involves the game at the field. It includes different aspects like food & beverage, apparel, sponsorship, licensing, events, tourism, and infrastructure (ATKearney, 2011). In North America this industry is one of the most important in terms of creating a positive impact to the economy, increasing surprisingly fast the GDP of the United States and Canada. \n \nThe United States and Canada are the world’s biggest sports nations that provide a wide range of sport facilities and infrastructure and hosts yearly enigmatic events in key cities like Boston, New York, Los Angeles, Vancouver and Toronto. For this reason, we identified that these countries are a strategic move for any sports-related company to keep growing within the Sports Industry. \n \nThe current report aims to provide a comprehensive research about the Sports Industry in North America, describing and analyzing possible investment opportunities in these countries for the upcoming years. \n \nThe document is structured to explain an I) Overview of The Sports Industry in the United States and Canada, including the main sports leagues, secondary sports, sport facilities and new technology and trends. Then, we will discuss about the II) Main Leagues in North America considering its main teams, athletes, events, and highlight sport cases. Finally, we will describe the III) Sports Media Industry in North America, explaining about the Print, TV, Radio, Online channels and current media trends.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.031 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.039 |
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".