Planning for your CANOE (Circumspect Awareness and Navigation of Outcomes and Expectations) journey in community-engaged research with Indigenous communities
Bibliographic record
Abstract
Community engagement has long been recognised as necessary for working with Indigenous communities. Although many researchers are excited to engage with communities and many articles describe the process of community engagement in research, almost none have addressed the foundational question of whether researchers should engage with Indigenous communities for research. In this Viewpoint, we will discuss the Circumspect Awareness and Navigation of Outcomes and Expectations (CANOE) approach, which describes what should be considered before embarking on a community-engaged research journey with Indigenous communities. We build on existing literature regarding understanding the need to recognise positionality, practise reflexivity, assess personal strengths and weaknesses, and consider abilities and skills that can be offered or promised to Indigenous partners. Our goal is to provide principles of being reflexive, intentional, and careful before launching into research with Indigenous communities. Drawing from our combined decades of experience as Indigenous, community-engaged scientists leading national and international community projects, we draw from the extant literature and lessons learned in the field to provide a guiding CANOE approach for community-engaged research. This Viewpoint provides researchers interested in community-engaged projects with the information they need to consider before embarking on their research journey. We provide a set of CANOE self-assessment questions designed to evaluate a researcher's preparedness, suitability to invest in a research partnership, and adaptability to navigate a research journey with Indigenous communities. Not only should relationships be properly developed and nurtured, but researchers need to fundamentally understand their ability to develop research partnerships that prioritise Indigenous cultural worldviews and protocols in research design, development, testing, and implementation.
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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 | 0.109 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.050 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".