Leveraging Events for Sustainable Community Participation: The 2014 Glasgow Commonwealth Games
Bibliographic record
Abstract
Drawing on a case study of the 2014 Commonwealth Games in Glasgow, this article examines the extent to which the hosting of an integrated parasport event where able-bodied athletes and athletes with a disability compete alongside one another is being leveraged to create opportunities for community participation, and influence community attitudes towards disability.The assumption about hosting parasport events is that the mere visibility of events will impact attitudes and perceptions towards persons with disabilities in a positive manner; however, little evidence beyond anecdotes supports this assumption.Recent research on leveraging events also suggests the need to strategically utilize the opportunity of the event and related resources if seeking to attain sustainable positive impacts for the host community [Chalip, L. 2006."Towards Social Leverage of Sport Events."Journal of Sport & Tourism 11 (2): 109-127.doi:10.1080/14775080601155126].Empirically, this article draws on extensive data collection undertaken before, during and after the 2014 Commonwealth Games, specifically an analysis of policy and legacy planning documents and strategic interviews conducted pre-Games examining the tactics, strategies and programmes used by stakeholders to enhance community participation opportunities.The results suggest that whilst at the strategic level there was evidence of an integrated policy approach to leveraging the event for broader accessibility outcomes, this was not always accompanied by clear programmes or projects that are likely to lead to demonstrable impacts beyond the normal temporality of large-scale sporting events.We conclude by suggesting that the absence of clear, resourced and measurable aspirations for the parasport element of the Games may lead to unfulfilled leveraging possibilities as levels of interest and resources diminish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".