Leveraging sport and entertainment facilities in small- to mid-sized cities
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how, once a city has made a decision to build a new arena, local stakeholders envision the venue as a leverageable asset to achieve broader development goals through event hosting. Design/methodology/approach A total of 66 semi-structured interviews were undertaken in 12 cities across Canada. Participants included city employees (parks and recreation, tourism), elected officials (current and former mayors, councilors), arena management, management from the local team (serving as anchor tenant), members of chambers of commerce and local business associations, prominent members of the local business community, and other politicians and relevant stakeholders (members of parliament, bloggers, journalists, educators, and community activists). Interviews were transcribed and subject to coding to identify themes. Findings Core themes were identified which captured how key stakeholders viewed the arena as an opportunity to leverage other events being targeted and held at the arena. This included: opportunities and benefits of hosting other events; the arena, competitiveness, and competition; partnerships and collaboration; capacity: knowledge and experience; and leveraging challenges. Originality/value This study makes several important contributions to the literature. First, it examines sports facilities in smaller cities, a subject more widely studied in larger, “major league” cities. Second, it takes a different approach to understanding leveraging, examining facilities rather than the event that the city is hosting or the franchise that plays in the city. Third, it examines a context where the facility has been built for a sports team, and not for other sport and entertainment events that might be hosted there.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".