BRIDGING INDIGENOUS KNOWLEDGE AND WESTERN SCIENCE: CO-CREATING BEST PRACTICES FOR COLLABORATIVE ENVIRONMENTAL RESEARCH
Notice bibliographique
Résumé
A co-creation framework was developed for non-Indigenous scientists and engineers aiming to conduct research with Indigenous communities. Developed from pre-existing CBPR and co-creation theories, this guide incorporated the personal experiences of two master's students working on this project. As Indigenous communities and individuals are not monoliths, the first draft of this framework was devised to be expanded for use with various other groups allowing researchers to apply relevant concepts specific to their projects. The co-creation framework was developed and executed by conducting an initial water quality analysis of drinking water from SN. Initiated by Duignan’s 2019 SN health survey feedback, preliminary water parameters were analyzed for select households across the community. Community services and members were instrumental in co-creating this style of data collection and knowledge translation with GWF researchers. Collections methods were primarily adapted due to the COVID-19 pandemic, in which researchers were led initially by community liaisons and taken to households to collect drinking water samples. Instead, homeowners were supported in collecting their own drinking water samples and providing them to community educators from SNHS. Concurrently, further development and application of the framework were established through an interactive video podcast, Ohneganos Let’s Talk Water, employed to conduct, disseminate, and translate relevant community research. The community-centred methodology met the target audience where they were, on social media, rather than expecting them to decipher conventional WS science dissemination methods such as academic conferences or peer-reviewed papers. International and transdisciplinary collaboration was explored between Indigenous and non-Indigenous youth, students, experts, artists and community members. This multifaceted, award-winning show was the first to combine these various elements. A mixed methods approach via digital story was produced to illustrate the impact of LTW. While an extensive variety of guests and topics were discussed in the four seasons of the podcast, the digital story highlights those most closely aligned with the work of this thesis, decolonizing western science research and dissemination.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».