Caught Stealing: Debunking the Economic Case for D.C. Baseball
Bibliographic record
Abstract
District of Columbia mayor Anthony Williams has convinced Major League Baseball to move the Montreal Expos to D.C. in exchange for the city's building a new ballpark. Williams has claimed that the new stadium will create thousands of jobs and spur economic development in a depressed area of the city. Williams also claims that this can be accomplished without tax dollars from D.C. residents. Yet the proposed plan to pay for the stadium relies on some kind of tax increase that will likely be felt by D.C. residents. Our conclusion, and that of nearly all academic economists studying this issue, is that professional sports generally have little, if any, positive effect on a city's economy. The net economic impact of professional sports in Washington, D.C., and the 36 other cities that hosted professional sports teams over nearly 30 years, was a reduction in real per capita income over the entire metropolitan area. A baseball team in D.C. might produce intangible benefits. Rooting for the team might provide satisfaction to many local baseball fans. That is hardly a reason for the city government to subsidize the team. D.C. policymakers should not be mesmerized by faulty impact studies that claim that a baseball team and a new stadium can be an engine of economic growth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".