Open Access: Where are the Indigenous and First Nations people in sport event volunteering? Can you be what you can't see?
Bibliographic record
Abstract
Mega-sport events (MSE) are frequently cited for their developmental and legacy potentials for host communities, including tourism, sport participation and volunteering. MSE volunteer research has demonstrated the potential to develop volunteers who may contribute to the host community’s social and human capitals. However, little research considers how marginalised groups, such as First Nations or those with disability, may be co-providers of MSE experiences. This paper differs from a dominant quasi-scientific approach to empirical journal articles in that it begins with a reflexive posture drawing upon First nations pedagogy of storytelling. Reflecting upon the volunteers’ social context and drawing upon a dataset of volunteers across 6 MSE in 5 countries (2009–2016), this research explores to what extent First Nations volunteers are considered and included in MSE research and practice, and what differences may exist between First Nations volunteers and others regarding their motivations and future volunteering intentions. The results indicate that significantly more can be done to include First Nations people equitably and respectfully across the design, delivery, and legacy potential of MSE. The results inform a novel framework that provides a map for theory and practice, and thus praxis, for incorporating marginalised groups as full partners across the MSE journey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".