Growth of Major League Soccer: Post-FIFA World Cup 2014
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
abstract: The U.S. sports market is and has been dominated by professional football, basketball, and baseball leagues. U.S. interest in soccer has exploded as the sport looks to establish its position in this saturated sports market. As a general consensus, Major League Soccer (MLS), the recognized professional soccer league in both the U.S. and Canada, is expecting increased growth following the 2014 FIFA World Cup. My goal is to track that growth from June 2014 and to monitor the league's responses to that growth. How do league executives manage growth? I am curious about the background finances- especially when heated negotiations are expected heading into a new collective bargaining agreement (CBA). The compelling question I am looking to answer is: How will the MLS market respond to growth in a highly saturated U.S. sports market, particularly after the 2014 FIFA World Cup?
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it