Is winter coming? Outdoor recreation voluntary associations and fat biking in Northwestern Ontario and Northeastern Minnesota
Bibliographic record
Abstract
Outdoor recreation voluntary associations (ORVAs) such as mountain biking associations play vital roles in the creation, management, and upkeep of trail systems in North America. While research on ORVAs has expanded in the last decade, studies have not sufficiently examined the challenges presented by the impacts of climate disruption on ORVAs, including increased demands on volunteers and event cancellations, nor the potential long-term impacts on the viability of trail-based activities coordinated by ORVAs. Based on interviews and surveys conducted in Northwestern Ontario (NWO) and Northeastern Minnesota (NEM), this study aimed to 1) ascertain the extent of fat biking participation in NWO and NEM and the ridership profiles of those engaged in this recreational activity, 2) assess their levels of engagement as volunteers within local ORVAs, 3) assess their willingness to volunteer in the future, and 4) explore the challenges and opportunities associated with the inclusion of fat biking as a climate change adaptive strategy within regional recreation offerings. Findings revealed that while fat bikers appreciated the volunteer efforts of trail groomers and event/race coordinators more than forty percent were unlikely to volunteer with local ORVAs. Existing ORVA volunteers reported higher demands on their time during heavy snow seasons, as well as burnout associated with a lack of volunteer recruitment and retention strategies. With climate disruption trends expected to continue, Mountain Biking ORVAs (MB-ORVAs) must proactively manage associated and compounded challenges by developing seasonal trail grooming and volunteer recruitment, management, and retention strategies and should consider rotating co-hosting duties for collaborative fat bike events to ensure the provision of safe and well-groomed trails, and regularly occurring events, which support the continued development and growth of regional winter fat biking engagement. By highlighting how fat biking is employed to provide year-round trail riding opportunities, this study expands on current understandings of Mountain Biking Outdoor Recreation Voluntary Associations (MB-ORVAs) in the U.S. and Canada. MB-ORVAs must proactively manage the challenges associated with climate disruptions and the increased demand placed on volunteer groomers and administrative capacities. MB-ORVAs should: • Continually assess fat bikers' perceptions of natural resource conditions (e.g., snow volume, frequency and severity of snow fall, depth of snowpack, etc.) within provided recreation settings, and the individual adaptive strategies fat bikers and other outdoor recreationists employ when faced with suboptimal conditions; • Assess the impact of fat bikers' perceptions of natural resource conditions and the severity of climate disruptions on their willingness to volunteer for trail grooming and event hosting initiatives; • Develop a binational/biannual fat biking event to distribute hosting responsibilities, reduce strain on volunteers and local MB-ORVA resources, and provide a platform to showcase existing and emerging fat biking trails in both regions; • Develop and implement an annual volunteer engagement and sentiment survey to solicit feedback on perceived volunteer workload, sentiment towards volunteering, and experiences of volunteering from both active and passive ORVA members; and, • Implement volunteer recruitment and retention strategies, including establishing a volunteer recognition program, developing targeted volunteer recruitment and retention plans, and hiring a dedicated volunteer coordinator to lead these initiatives. Adopting these strategies will position MB-ORVAs in NWO and NEM, and other regions impacted by climate disruptions, to deliver high-quality winter recreational experiences, including safe, well-groomed trails, and regularly occurring events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".