Professional sports venues as catalysts for revitalization? Perspectives from industry experts
Bibliographic record
Abstract
Throughout North America, professional sports venues (PSV) have become a prominent urban redevelopment strategy. Much of the previous research related to PSV has focused on economic impacts and the use of public money to help fund construction. Conversely, there is less research exploring the relationship between PSV and the urban environment. To better understand this relationship, we disseminated a web-based survey to planning and development practitioners. Our research explores the degree to which industry experts: (1) perceive PSV as catalysts for development and revitalization; (2) associate PSV siting, policies, and programs with development and revitalization objectives; and (3) believe PSV investments in different (sub)urban locales can affect neighborhood change. Our findings illustrate a high degree of expert optimism, with respondents reporting that they believe PSV have the capacity to generate place-based changes. Their responses support a downtown-centric perspective, as professionals see greater opportunity for PSV to drive change in downtowns relative to more peripheral locations. Notably, respondents indicate that municipalities play a prominent role in supporting PSV outcomes, primarily through the adoption and implementation of key land use, zoning, and planning policies.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".