Editorial: Co-creating knowledge for community resilience to sustainability challenges
Notice bibliographique
Résumé
• Improved conceptualization of problems and the social-ecological systems in which they are embedded • local ownership of and public trust in knowledge co-created • fairness -those affected by research results are included in equitable processes for knowledge generation • nurturing safe spaces for just solutions • upholding the rights and integrity of Indigenous Peoples, particularly those who have customary and/or legal rights and territories, responsibilities, and duties • increased uptake of knowledge leading to more effective and innovative solutions to sustainability challenges • restructuring institutions and relationships for sustainable transformations (see Chambers et al. 2022;Nörstrom et al. 2020;Reed et al. 2023;Satterthwaite et al. 2024;Wyborn et al. 2019). All of these reasons feature in one or more of the articles of this special issue.This Research Topic highlights multiple approaches and key lessons learned when researchers explicitly seek to co-produce knowledge with community partners to build resilient landscapes, communities, and social-ecological systems. Articles illustrate community-academic partnerships that have shaped practices on the ground or policy implementation across a diverse set of circumstances and locations around the world.Maria Paula Sarigumba and colleagues illustrate the value of a co-production approach to strengthening the engagement of youth in territorial governance in an Indigenous community in Brazil. Their approach was an emergent one, with several phases of work over a five-year period. Their article points to the need for researchers to take direction from their community partners to assure local and lasting benefits.Luigi Conte and colleagues also used a staged approach, seeking to find key leverage points for advancing an agroecological transition in Western Sicily. Engaging both farmers and scientists in a participatory action research initiative enhanced understanding of local ecological and social values and helped all participants explore new ways to address mistrust that had characterized top-down scientific practice and regional management of agro-ecosystems. Hence, both farmers and scientists benefited, as farmers enhanced their awareness and capacity to share the ecological and social values of their experiences, and scientists explored how best to address historical mistrust as a consequence of former top-down approaches to structural change.The research by Pinho Tiago and colleagues takes a very different approach, focusing on how citizens contribute to environmental science through Bioblitzes in urban municipalities (Lisbon, Oeiras, and Almada) within the metropolitan area of Libson, Portugal. Bioblitzes are community science events where people learn about conservation issues while registering species observations and academic experts gain value through greater understanding of and appreciation for both ecological change and socio-cultural context (Roger and Klistorner 2016). Their findings demonstrate the potential to diversify participants in BioBlitzes with recommendations aimed at strengthening knowledge co-production.Tina Elliott and colleagues' contribution offers quite a different strategy for knowledge coproduction. They apply feminist theory as a methodology to support a knowledge coproduction process with rural residents who have experienced wildfire. They demonstrate how a researcher can become a "collaborator" along with rural participants while creating new knowledge about local change and adaptation. Researchers and community members learned together who and how different social groups were affected by the wildfire, offered concrete strategies for sharing knowledge, designed a framework and a guidebook that can help community members create local adaptation solutions for today and the future.Eureta Rosenberg and colleagues' paper illustrates that attention to, and demonstration of, relational factors are critical for successful collaborations when university-based scientists and local people work together to monitor and manage environmental change. Focusing on a project that was deemed successful in achieving collaborative sustainable natural resource management in the Tsitsa region of the Eastern Cape Province of South Africa, the authors point to several relational factors that characterized success. Relational factors such as care, respect, and trust were evident in practices such as addressing local people in their own language, ensuring scientists followed-up with local people, and paying local environmental monitors for their work. Efforts to strengthen relationships between scientists and local monitors also revealed that all participants shared key values such as family, stimulating work, and stewardship of the environment. While relational considerations have historically been overlooked by natural scientists, Rosenberg and colleagues point out that attention to relationships is necessary for successful collaborations, particularly in regions where mistrust and societal divisions have characterized landscape management in the past.Finally, the reflection by Marchelo Leguia-Cruz and colleagues explains how co-productive reflection processes sought to enhance participatory governance in La Campana-Peñuelas Biosphere Reserve in Central Chile. They focused on reflections of young participants (youth) and Indigenous Peoples who participated in an Open Academy between 2019-2023. They employed Participatory Geographic Information Systems to visualize governance challenges and opportunities. They applied both traditional metrics of assessment with principles of transdisciplinary and intergenerational knowledge co-creation to reveal gaps in the governance performance and make recommendations. Following severe wildfires in 2024, they noted that the Biosphere Reserve Management Committee had been reactivated with representatives from civil society and Indigenous communities, suggesting the beginning of a new era for participatory governance in the region.In different ways, the articles tell us overlapping stories about how to create knowledge with one another. Key lessons include taking the time to build trust, engaging diverse knowledge holders and knowledge systems, sharing power, and facilitating learning among researchers and community partners. These lessons are easy to summarize but challenging to put into practice. Participatory co-production projects demand that we spend more time building and nurturing research relationships, tailoring engagement efforts over time and to different contexts, being mindful that researchers must share power in co-production research relationships, and allowing researchers to be guided by the needs, interests, responsibilities, and rights of community partners. These practices require patience and humility to facilitate learning among all research participants -including researchers themselves (see also Reed et al. 2023;Satterthwaite et al. 2024). Applying these strategies offers the possibility of robust, innovative, creative, and impactful research results strengthened by a diversity of actors, knowledge systems, perspectives and approaches. Knowledge co-creation also offers practical benefits that enhance sustainability by building capacity through improved relational skills, offering outputs that are meaningful to community partners, and empowering Indigenous and local people to take action for sustainability by enhancing skills and knowledge necessary for decision making and leadership. In this collection, we hope you will find practical examples that inspire your own journeys in knowledge co-production for a sustainable, resilient, and just future.
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 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 | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».