Have the Olympics outgrown cities? A longitudinal comparative analysis of the growth and planning of the Olympics and former host cities
Bibliographic record
Abstract
This paper examines the growth of the Olympic Games against that of former host cities to understand whether this mega-event may have 'outgrown' its hosts.The increasing hosting requirements and governments' expansive use of mega-events as tools for urban development would suggest that the 'Olympic city'a term we use for describing the size of the Olympics as hosted in different cities over the decadeshas grown at a faster rate than former host cities.The analysis contrasts historical indicators that capture the evolving size of planning for the event based on four dimensionssport, spectators, marketing and costsas well as the urban dimension of hosting experiences (venues and infrastructure) with city trajectories based on demographic and economic indicators.This is done through a longitudinal analysis of former Olympic host cities from the 1960s and 1970s and from which continuous longitudinal data are available: Tokyo, Munich, and Montreal.The findings indicate that the Olympic city has grown more strongly than these former host cities, although not uniformly across trajectories.This gives evidence for the need to review the size of mega-event impacts if they ought to continue to generate interest in hosting them in the future.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".