Leveraging parasport events for community participation: development of a theoretical framework
Bibliographic record
Abstract
Research question: Sporting events have become highly sought after tools for economic, tourism, and social development in cities around the world; however, little is understood about how to effectively leverage events. This purpose of this paper is to present a framework for leveraging small-medium scale sporting events for increasing community accessibility and opportunities for persons with a disability to participate in sport, recreation, and leisure.Research methods: Drawing upon interview and documentary data that are part of a broader research project that examines how small-medium scale sporting events have and can be leveraged to enhance accessible opportunities in local communities, the data were reanalysed focused postevent learning in regards to leveraging strategies for future event opportunities in order to build the framework.Results and findings: The Parasport-leveraging Framework is presented here as an emergent leveraging structure that shifts the focus of parasport event legacies away from the notion that merely hosting a disability sport event will impact upon communities' understanding and relationship with disability sport. Further, the framework emphasizes the need to strategically use the events in the context of policy frames and discourses of event-related processes.Implications: This research furthers the work of Chalip, Smith, and others who have argued that in order for benefits to accrue to a host region, host communities need to find points of leverage within the context of event resources to enhance positive social legacies. Additionally, it offers a strategic starting point for communities seeking to implement leveraging plans alongside event agendas.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| 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".