The social impact of participative sporting events: a cluster analysis of marathon participants based on perceived benefits
Bibliographic record
Abstract
Since many years, hosting mega-events is known to have potential positive effects on local communities. In the recent years, there has been a growing interest for non-strictly economic impacts, among which well-being, quality of life, sense of belonging, civic pride (Crompton 2004 Crompton, J. L. 2004. “Beyond Economic Impact: An Alternative Rationale for the Public Subsidy of Major League Sports Facilities.” Journal of Sport Management 18 (1): 40–58. doi:10.1123/jsm.18.1.40.[Crossref], [Web of Science ®] , [Google Scholar], Balduck, Maes, and Buelens 2011 Balduck, A. L., M. Maes, and M. Buelens. 2011. “The Social Impact of the Tour de France: Comparisons of Residents’ Pre- and Post-Event Perceptions.” European Sport Management Quarterly 11 (2): 91–113. doi:10.1080/16184742.2011.559134.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar], Kim and Walker 2012 Kim, W., and M. Walker. 2012. “Measuring the Social Impacts Associated with Super Bowl XLIII: Preliminary Development of Psychic Income Scale.” Sport Management Review 15 (1): 91–108. doi:10.1016/j.smr.2011.05.007.[Crossref], [Web of Science ®] , [Google Scholar]) as well as destination image (Alonso-Dos-Santos et al. 2014 Alonso-Dos-Santos, M., F. Calabuig, F. Montoro, I. Valantine, and A. Emeljanovas. 2014. “Destination Image of a City Hosting Sport Event: Effect on Sponsorship.” Transformations in Business and Economics 13 (2): 161–173. [Google Scholar], Armenakyan et al. 2012 Armenakyan, Anahit, Louise A. Heslop, John Nadeau, Norm O’, N. A. Reilly, and Irene R. R. Lu. 2012. “Does Hosting the Olympic Games Matter? Canada and Olympic Games Images before and after the 2010 Olympic Games.” International Journal of Sport Management and Marketing 12 (1/2): 111–140. doi:10.1504/IJSMM.2012.051265.[Crossref] , [Google Scholar], Berkowitz et al. 2007 Berkowitz, P., G. Gjermano, L. Gomez, and G. Schafer. 2007. “Brand China: Using the 2008 Olympic Games to Enhance China’s Image.” Place Branding and Public Diplomacy 3 (2): 164–178. doi:10.1057/palgrave.pb.6000059.[Crossref] , [Google Scholar]). Most of the studies have investigated these effects through spectator events. Researches regarding participative events are much less developed. Hence, this article seeks to delve into this area, more particularly by wondering what impacts participative events can have on the participants themselves. Based on a literature review that identifies three main areas of impacts (i.e. city image, sport participation, and psychosocial benefits), a questionnaire was built and submitted to the participants of the Unicef Geneve Marathon (N = 1305). A statistical segmentation (cluster analysis) procedure was performed, which allowed for the identification of three distinct groups of participants based on a combination of eight factors. Each of these groups are described, thereby confirming the existence of a variety of effects related to participative sporting events that are then discussed both from theoretical and managerial perspectives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".