Places that Glow: Evaluating the Legacy of the 2015 Toronto Pan/Parapan American Games
Bibliographic record
Abstract
Toronto is two years removed from hosting the 2015 Pan/Parapan American Games. Within the two-year span, from the event's commencement to its end, ample time has been given to debate and consider whether the legacy of the Games has benefitted or hindered wider municipal and regional planning goals. Both the City of Toronto and the Greater Toronto Area witnessed the mounting of large infrastructure projects (mega sporting infrastructure) across city and regional landscapes. Each project differs, however, as they provide different benefits (or none at all) and produce separate outcomes that impact local communities and tie together physical and social goals. This paper investigates the legacy of the 2015 Pan/Parapan American Games and examines how infrastructure built for the Games contributes to a positive or negative legacy. \n \nA case study of the Toronto Pan Am Sports Centre in Scarborough presents the impacts of Pan Am infrastructure on the overall legacy. The relation between the Pan Am overall legacy and success of the Toronto Pan Am Sports Centre confirms that the post-Games positive legacies are virtually tied to inclusive and responsible planning practices. This relationship also shows that cities choosing to host mega sporting events run the risk of cost overruns and mistimed project goals. Whether the outcomes of economic and social promises lay solely on the municipality or not, the success of these events, and eventually, their legacies, are tied directly to governmental commitment and increased partnerships.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".