Building common ground: learning and reconciliation for the shared governance of forest land in northwestern Ontario
Notice bibliographique
Résumé
Historically, First Nations in Canada have been marginalized with regards to the governance of forests. This has contributed to racial tensions in places such as northwestern Ontario, where First Nations and settlers have come into conflict over land allocations and forestry practices, causing a great need for reconciliation within the institutions built around forests. Recent socio-economic shifts have influenced forest tenure reform in Ontario, and in the northwest this has contributed in part to examples of collaborative forest governance involving First Nations, as well as First Nations involvement in the business side of forest management. This research used a case study approach and considered two interconnected case studies. The first is Wincrief Forestry Products Ltd., a forest products company that is 49% industry owned and 51% First Nations owned. The second is the Miitigoog General Partner Inc., a larger collaborative organization (inclusive of the first) that was set up to manage the Kenora Forest through a Sustainable Forestry License with decision-making authority shared equally by First Nation and industry partners. The purpose of my research was to understand the implications of transformative learning within cross-cultural settings, particularly how such learning can inform collaborative governance of shared land and resources. Qualitative methods were used, including document review and semi-structured interviews with key informants and those involved in governance [n=43]. Data related to governance were analyzed using institutional mapping, and other data were coded according to the learning and transitional justice literature. Findings are presented as learning outcomes and processes, and contextualized forms of learning relating to cross-cultural collaboration. The research makes several contributions to understanding governance, learning and reconciliation within the context of cross-cultural forest management. Key results included evidence of the importance of informal learning and learning together prior to formal collaboration. Another key result was that learning outcomes may depend on when partners enter the collaboration, and in relation to this that working through initial conflicts were an important aspect of learning through collaboration. This research highlights the importance of learning-by-doing, connections between culture and learning, and the importance of “two-row” (non-assimilative) and decolonizing approaches to understanding cross-cultural learning.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».