Municipal Perspectives on Collaboration in Regional Sport Event Hosting: A Case Study of the Niagara 2022 Canada Summer Games
Bibliographic record
Abstract
Increasingly, sport event bids indicate that multiple jurisdictions within a given region will collaborate on hosting efforts, so that they can share the risks, leveraging opportunities, and benefits of hosting. However, such hosting arrangements are complex and involve many stakeholders, including municipal departments. In this case study, we examine the perspectives of municipal actors involved in a regional approach to hosting the Niagara 2022 Canada Summer Games. Framed with concepts of collaboration and organizational capacity, we used social network analysis and semi-structured interviews to collect data. Our findings include a sociogram as well as a discussion of: (1) buying in to a regional approach; (2) addressing variability in size, scope, and capacity across municipalities; (3) networking and communication among municipalities; and (4) assessing the regional hosting approach. Through this case study, we contribute a nuanced understanding of municipal actors’ perspectives and experiences of collaboration in the regional hosting process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 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".