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Enregistrement W7027445912

Collaboration, Knowledge-Sharing and Natural Hazard Risk Management in the Greater Pinery Provincial Park Region

2021· article· en· W7027445912 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholars Commons (Wilfrid Laurier University) · 2021
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueTravel Writing and Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNatural hazardHazardRisk managementNatural (archaeology)Natural disasterEnvironmental hazardRisk assessment
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In Canada, parks and other forms of protected areas are visited by tens of millions of people annually. By their very nature, parks and protected areas present various risks that must be mitigated. Risk management is an area of research that is receiving increased attention because in recent years, natural hazards (e.g. effects the environment) and disasters (e.g. effects the environment and humans) have exacted significant economic, social, health, cultural, and environmental impacts on persons and communities across Canada. Natural hazards, which pose significant risks to the natural environment and those individuals who inhabit or visit them, are expected to increase in occurrence and severity because of climate change. Knowledge and knowledge management is vital to making informed decisions that aim to protect the health and well-being of visitors. Understanding the value in and having the capacity to access and use various forms of knowledge (including social science, natural science, local, Indigenous, and other forms of knowledge) when making decisions to help proactively and reactively respond to natural hazard risks is critical now and in the future.\nThis thesis focuses on tornadoes and wind events in the greater Pinery Provincial Park region to understand the risks that these natural hazards pose to visitors and how to best adaptively mitigate such risks in the face of climate change. The thesis employed a qualitative case study approach, which examined how knowledge of natural hazard risk management is (or is not) used, produced, shared, and managed within the greater Pinery Provincial Park region. Informing the research study design were Nguyen et al.’s (2017) Knowledge-Action Framework and Bennett et al.’s (2016) Framework for Collaborative and Integrated Conservation Science and Practice. 15 key informants participated in semi-structured, in-person interviews, that were collaboratively coded using an adapted version of Braun and Clarke’s (2006) inductive, thematic approach, aided by NVivo 12 software, to discover 12 main themes, including: 1) Adaptive Management, 2) Collaboration and Partnerships, 3) Communication, 4) Knowledge Acquisition, 5) Knowledge Integration and Decision-making, 6) Knowledge Sharing and Exchange, 7) Planning, 8) Plans, Policies, and Regulations, 9) Relationships, 10) Resources and Capacity, 11) Responsibility and, 12) Risk Monitoring and Evaluation.\nThematic coding findings identified a number of diverse strengths (e.g., transparent internal communication and integration of natural science knowledge into decision-making), weaknesses (e.g., lack of collaboration and partnerships with Indigenous communities and an absent understanding of social science and its’ value), and needs and opportunities (e.g. involvement of Public Health sector stakeholders in natural hazard risk management and more dedicated resources to support capacity).\nThe Sendai Framework for Disaster Risk Reduction 2015-2030 was chosen to deductively frame the discussion. The goal of the Framework is to: “prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience” (UNDRR, 2015, p. 11). The Framework’s first three priorities focused on: 1) understanding disaster risk, 2) strengthening disaster risk governance to manage disaster risk, and 3) investing in disaster risk reduction for resilience, effectively guided the discussion of the 12 main themes identified from the key informant’s in-person interviews (UNDRR, 2015). The Framework’s fourth priority focused on enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation, and reconstruction (UNDRR, 2015).\nNumerous detailed immediate-term and short-term recommendations, based on the 12 main themes identified through the inductive, thematic analysis, are put forward, including: 1) focusing efforts on proactive natural hazard risk management planning in anticipation climate change effects; 2) integrating social science knowledge, traditional ecological knowledge, Indigenous knowledge and other forms of knowledge more effectively into natural hazard risk management decision-making; 3) involving, more effectively, Public Health sector stakeholders in natural hazard risk management planning; and, 4) increasing availability and access to financial resources to support risk management activities. The implementation of such recommendations would support more effective adaptive natural hazard risk decision-making, planning and management in the region. Overall, the research study addresses a critical gap in parks and protected area and natural hazard research. It represents the first known study in Canada to examine how knowledge of natural hazard risk management is (or is not) used, produced, shared, and managed within a greater protected areas context.

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.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,944
Score d'incertitude au seuil0,778

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,205
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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