International Mega-Events and Urban Planning in the Context of Toronto
Bibliographic record
Abstract
This paper explores mega-events and their relationship to urban planning and public participation. Mega-events, often referred to as hallmark events, are short-term, high profile spectacles that have a massive popular appeal, a large mediated reach, and international significance (Hall, 1992; Roche, 2000). Mega-events include major fairs, festivals, expositions, such as the World Expo and significant sporting events like the Olympic games and the FIFA World Cup. For many cities, mega-events are an alluring urban strategy that “promises” tangible and intangible benefits for cities and nations (Burbank et al., 2001). These short-lived events can have tremendous influence over urban spaces, built environments, and city populations (Greenhalgh, 1988; Roche, 2003). Given their impacts, it should not be a surprise that these events have encountered various forms of resistance (Lenskyj, 2008; Cottrell, 2011; Gotham, 2016). A significant amount of this opposition focuses on the lack of accountability, transparency, and public engagement that is often seen in the various mega-event hosting processes (Kidd, 1992; Flyvbjerg, 2003; Hall, 2006). Those that oppose these events critique the undemocratic nature of decision-making processes used to bid for and plan hallmark events (Kidd, 1992; Gotham, 2011). Through this essay, I will argue why participatory planning strategies must be used for the development of inclusive decision-making processes in mega-event planning within the city of Toronto. I will argue that although public engagement and a commitment to participatory planning has seemingly been devalued in the city’s history of pursuing the hosting of a hallmark event, they are essential components for the successful and equitable bidding and planning of such events. I believe participatory planning can be used for the meaningful consideration of various public interests and the creation of a “hosting concept/vision” that works towards the advancement of varying city priorities across a wide range of local communities. When thinking of how to engage varying communities in mega-event planning processes, it is vital to consider what engagement approaches have been used in previous mega-event hosting attempts, and what future strategies are recommended for the city of Toronto.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".