Sport event hosting capacity as event legacy: Canada and the hosting of FIFA events
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how a legacy of event hosting competencies from one event can contribute to advancing the overall hosting capacity of a nation for future events. More specifically, the project focuses on determining the event hosting capacity legacies from the Men’s Under-20 2007 Fédération Internationale de Football Association (FIFA) event in Canada and how they contributed toward winning the rights for the Women’s FIFA World Cup 2015 event. Design/methodology/approach A qualitative case study design focusing on FIFA events held in Canada in 2007 and 2015 was used. Findings Four broad event hosting capacity legacies from the U-20 2007 event that potentially impacted Canada’s ability to secure the WWC 2015 were identified. These legacies included: exemplifying success, advancement of hosting concepts, staff and leadership experience and development and enhancement of sporting infrastructure. Research limitations/implications The findings formed the basis of a discussion on the increasing formalization of event organizing committees, the need to consider collective (i.e. multiple events) legacies in the development of hosting strategies as well as the importance of developing the trust of the local community to support future sport event bids and hosting. Originality/value The originality and value of this research paper lies in its use of empirical case study findings to illustrate the potential for hosting capacity legacies of sporting events as well as the level and type of event under investigation (i.e. large-scale, football/soccer).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".