Volunteers, Place, and Ultramarathons: Addressing The Challenge of Recruitment and Retention
Bibliographic record
Abstract
Ultramarathons are often hosted in peripheral areas featuring challenging natural landscapes. Given limited local volunteer pools in these areas, the recruitment and retention of visiting volunteers is crucial to the sustainability of these events, yet little is known about the importance of the destination or place in terms of the volunteer experience. Therefore, the purpose of this study was to gain insight into the role that place plays in volunteer experiences at an ultramarathon in a peripheral area. A case study methodology was adopted with a focus on volunteers at the Canadian Death Race in Grande Cache (GC), Alberta, Canada. Semistructured interviews with event hosts, local volunteers, and visiting volunteers provided insight into the place dimension of the volunteer experiences. In phase 1, interviews with event/community hosts confirmed that local volunteer retention was challenging due to the growing demands of the event and to local volunteer fatigue. A systematic thematic analysis in phase 2 found that volunteers were connected to the destination through the place-based themes of: 1) beauty, 2) remoteness, 3) event, and 4) community. These findings demonstrated that "place mattered" in the experience of local and visiting volunteers. Therefore, organizers should actively recognize the importance of place when recruiting and retaining volunteers for these types of events in remote communities.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".