Collaborative Research with First Nations in Northern Ontario: The Process and Methodology
Notice bibliographique
Résumé
INTRODUCTIONThe importance of engaging with Indigenous peoples1 in climate change research is evident. Studies have brought attention to the valuable knowledge of Indigenous peoples in climate change adaptation and mitigation (Cruikshank 2001, Macchi et al. 2008), the adaptive capacities of Indigenous peoples to climate change (Devkota, Bajracharya, Narayan, Cockfield & Upadhyay, 2011, Galloway, McLean, Ramo-Castillo & Rubis, 2011) and in the 'co-production of knowledge' on climate change (Berkes 2009). Other studies have discovered drawing on Indigenous knowledge produces a better understanding than western knowledge and scientific methods alone, despite the challenges with integrating the two knowledge systems (Cochran et al. 2008; Bohensky & Mam 2011). Increasing there is the recognition that local and Indigenous knowledge fills gaps in scientific knowledge in remote or hard to access environments, particularly in areas of high environmental priority (Brook & McLachlan 2008). Incorporating the depth and breadth of Traditional Ecological Knowledge (TEK)2 provides invaluable insight on the historic and current status of the land (i.e., biophysical, flora and fauna) and across large geographic areas. Argued by Dowsley (2009) this insight not only expands scientific knowledge, but is necessary in forming appropriate policies in rapidly changing environments. The growing literature from Indigenous scholars also places local experiences in a broader context and is therefore relevant as a knowledge paradigm parallel with western knowledge (Henry at al. 2013) to address climate change.In recent years, Nishnawbe Aski Nation (NAN), a political territorial organization (PTO) representing 49 First Nations in Ontario, Canada, recognised the importance of addressing climate change and its impacts on their communities. First Nations in NAN are Cree, Ojibwe, and Oji-Cree people and parties to the historic Treaty No. 9 and No. 5 (Ontario portion); their territorial homeland covers 2/3 of the province of Ontario spanning -544 000 km2 (NAN 2014). NAN First Nations are predominantly forest-dwelling Aboriginal peoples and the rapid and unpredictable changes on the landscape related to climate change are influencing their traditional activities, food and energy security (Lemelin et al. 2010a; 2010b; 2010c; Golden, Audet, & Smith, 2015). This paper presents the process and methodology of our research collaboration and reviews the fieldwork experience with First Nations in northern Ontario to examine climate change on territorial land. The research collaboration applied Participatory Action Research (PAR) and was supported by the methodological philosophy CREE: C-capacity building, R-respect, E-equity, and E-empowerment (Lemelin & Lickers 2004). The research experience and lessons learned from the collaboration are woven together in the discussions; some research findings are also included in the discussion. The aims of this paper are to evaluate how the research method worked, or did not work, in practice and present best practices for collaborative research with First Nations affected by climate change.During a study the researcher and the research participants are interrelated (Creswell 2007) and interactively exchange data during the research process (Charmaz, 2006; Bernard & Ryan 2010). The recognition that researchers and participants are interrelated and interactive contributors in research presents the opportunity and basis for research collaborations. Research collaborations provide the space and avenue to exchange, on the one hand, a better understanding in the premise and requirements in academic research, and on the other, guidance in research inquiry that is sensitive and beneficial to everyone involved. Past research with Indigenous peoples (and other vulnerable or marginalized groups in society) has not always been respectful, indifferent, and at times, unethical (Cochran et al., 2008). …
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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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».