Bibliographic record
Abstract
Why do municipalities bid for mega-events? Simply bidding for these events, such as the Commonwealth Games, the Olympic Games or a World Expo, can run into the millions of dollars. The cost of hosting such a large-scale international event now runs into the billions of dollars. It would appear to be an economic risk, yet cities, and their respective countries, around the world continue to choose this public policy path. Using urban regime theory, and focusing on the work of Stone, Stoker and Mossberger, this research investigates the actors and their motivations surrounding the Commonwealth Games bids by Melbourne, Australia for 2006, Halifax, Nova Scotia for 2014, and Hamilton, Ontario for 1994, 2010 and 2014. Civic pride, economic development, tourism growth and infrastructure improvements are all motivating factors and a mega-event is seen as a short-cut to achieving these public policy goals. We conclude that strong cooperation between the public and private sectors is necessary, as well as comparable cooperation between the upper levels of government and the host city, for a seriously competitive bid in a Western democracy, and that the weaker the cooperation, the less resolve and likelihood there is to host an expensive event at any cost. This research not only furthers political science knowledge in the sports public policy field, but also confirms the use of urban regime theory as a useful framework in comparative urban analysis as it allows us to categorize actors and motivations as we compare across municipalities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".