Conducting a Market-Demand Analysis: A Case Study of Major League Soccer Expansion
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
Major League Soccer (MLS) has announced intentions to expand the league from 24 to 28 teams by 2022, with hints of further expansion to come. Expansion will allow the league to capitalize on the growing ticket and merchandise sales in new markets. League officials have 3 major considerations when choosing a city for expansion, which include a committed local ownership group with a passion for soccer and sufficient resources, a desirable geographic market with a history for supporting soccer and other sporting events, and a comprehensive stadium plan. Twelve cities across the country have submitted bids for expansion teams. Given the proposed bids, MLS needs to review the cities to determine which markets provide the highest likelihood of financial prosperity. As bid groups prepare their proposals for the committee, they will need to conduct a market-demand analysis. Each city will then be evaluated based on how well it meets the 3 criteria outlined by MLS, as well its ability to garner financial success.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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