Willingness To Pay For Downtown Public Goods Generated By Large, Sports-Anchored Development Projects: The CVM Approach
Bibliographic record
Abstract
North American cities have long encouraged redevelopment of their downtown cores to counteract the flight of residents and business to the suburbs in the postwar period. Building subsidized arenas and stadiums for professional sports teams downtown became common in the 1990s. In recent years, downtown stadiums and arenas have been proposed as components in larger redevelopment projects containing a number of other amenities, as well, including housing and other entertainment attractions. The justification for such developments rests in part on the public goods generated by vibrant, prosperous downtowns. Yet little is known about the value of such downtown public goods. This paper reports the results of two Contingent Valuation Method surveys to determine willingness to pay for new National Hockey League arenas in downtown Edmonton and Calgary in the Canadian province of Alberta. The hypothetical scenarios in both surveys varied to include affordable housing, a casino, and cultural space in addition to the arena. The surveys provide the first estimates of willingness to pay for downtown public goods for sports arenas, and also provide the first estimates of scope effects, that is, the willingness to pay for expansions of public goods, in the sports economics literature.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".