Understanding the Sport Event Volunteer Experience In the Implementation Mode of a Para-sport Event: An Autoethnography
Bibliographic record
Abstract
Research on volunteerism is one of the largest areas of inquiry within sport event management. Yet, the volunteer experience, as a phenomenon related to four constructs (satisfaction, motivation, commitment, and sense of community), is poorly understood over the course of the event's life cycle due to the strong emphasis on quantitative methodologies and cross-sectional designs. Using an autoethnographic approach, the purpose of this study was to understand the volunteer experience in the implementation mode of the event life cycle. The context of the study was the 2017 Canadian Electric Wheelchair Hockey Association Nationals where the first author collected data through field notes and a personal journal, while the second author acted as an outsider and allowed for peer debriefing to occur. Following the completion of the event, data were thematically analyzed and two key themes were identified: (a) success in assigned role: satisfaction and the volunteer experience, and (b) sense of community: impetus for commitment, motivation, and the volunteer experience. Theoretical contributions of this article include (1) the transferability of the conceptual framework used in the study, which was originally developed and investigated in the planning mode of the event life cycle; and (2) the understanding of the volunteer experience during the implementation mode and how it is impacted by its four related constructs. Event managers are encouraged to develop specific strategies touching upon satisfaction, motivation, commitment, and sense of community to enhance their volunteers' experience.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".