Bibliographic record
Abstract
With the increasing involvement of state and local governments in the professional sports industry over the last quarter of a century, the debate has arisen over whether the luring of a professional sports franchise or the construction of a stadium for a professional sports franchise provides any type of significant economic stimulus to a city. There are those who have engaged in this debate who believe the potential impact of these events to be significant and positive for a city. There are others who believe the potential impact of these events to be insignificant and/or negative for a city. The goal of this thesis is to add to the debate by presenting an econometric analysis of whether or not introducing a professional sports franchise and/or constructing a stadium for a professional sports franchise has any effect on a citys employment level. Our research based on taking data for each of the four major professional sports (Basketball, Baseball, Football, and Hockey) for various cities from 1979 to 1999 provides some very interesting results. The results of our econometric analysis suggest that building a new football stadium in a city or luring a basketball or hockey franchise into a city has a negative impact on a citys employment growth rate. However, our results also indicate that building a new basketball or hockey arena in a city for a current franchise or attracting a new football franchise to a city has a positive impact on a citys employment growth rate. Our research concludes that depending on the professional sport and the event involved the impact on employment in a city may be positive, negative, or not significant at all. Results that to a certain degree contradict previous econometric studies on the subject. iii ACKNO...
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.050 |
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".