Short-term Gain And Long-term Pain: A Case Study Of The 2015 Toronto Pan American Games And \nThe Union-pearson Express
Bibliographic record
Abstract
Mega-events have been credited with being catalysts of urban regeneration and accelerating infrastructure development. Staging a mega-event not only requires significant investment in event-related facilities but also usually necessitates upgrades to transportation infrastructure. This paper broadly examines the role of mega-events in fast-tracking urban improvements as well as the ramifications of accelerated development on cities. In particular, it discusses how the 2015 Pan American Games held in Toronto fast-tracked the completion of the Union-Pearson Express, a rail link connecting the city's downtown and primary airport, after the project had been stalled for years. This case study reveals the tensions between the long-term planning goals of the host city and more short-term demands for mega-events. The Union-Pearson Express is criticized for being inconvenient, inaccessible and over-priced, resulting in adverse impacts on the environment and human health and not doing enough to encourage public transportation. This paper contends that the Union-Pearson Express offered short-term gain associated with the Pan American Games that fails to address the long-term transit and other needs of the Greater Toronto Area. It is very much short-term gain for the price of long-term pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".