Is winter coming? Outdoor recreation voluntary associations and fat biking in Northwestern Ontario and Northeastern Minnesota
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».