BRIDGING INDIGENOUS KNOWLEDGE AND WESTERN SCIENCE: CO-CREATING BEST PRACTICES FOR COLLABORATIVE ENVIRONMENTAL RESEARCH
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.007 | 0.042 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".