Strategic groups of MLB ballparks: tourism perspectives
Bibliographic record
Abstract
This article presents the argument that sporting venues embody a diverse range of interpretations that have yet to be fully explored and understood by decision-makers and scholars alike. This paper fills this research gap through an in-depth environmental scanning process of the 30 North American Major League Baseball (MLB) ballparks. We employed a classification, cluster analysis, and strategic grouping approach to synthesize and organize knowledge. The classification process involved developing a set of 30 criteria to distinguish these ballparks from a tourism perspective, focusing on the experience within the ballpark as designed and presented by the organization to potential visitors. The cluster analysis reveals the presence of three groups of ballparks (Game-Centric Ballparks, Engaging Ballparks and Memorabilia Ballparks), each characterized by its own unique set of traits and behaviors. These clusters help in understanding how different ballparks attract and cater to various segments of tourists and sports enthusiasts. The key finding of this strategic group analysis is that ballparks fit into the following categories based on year of opening and user-generated ratings: beloved vintage ballparks, esteemed retro classic ballparks, fair contemporary ballparks and favored current ballparks. This triple approach – classification, cluster analysis, and strategic grouping – distinguishes between ballpark profiles and offers valuable insights into how ballparks can be strategically positioned and marketed as tourist attractions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".