Collaboration between local Indigenous and visiting non‐Indigenous researchers: Practical challenges and insights from a long‐term environmental monitoring program in the Canadian Arctic
Bibliographic record
Abstract
Abstract There is a growing appreciation for the value of collaborative research projects involving local Indigenous and visiting non‐Indigenous researchers. Examples of such partnerships are now numerous and diverse, and best practices and respectful approaches have been well presented, including the five priorities of the National Inuit Strategy on Research (NISR) defined by Inuit Tapiriit Kanatami in Canada. However, the application of best practices remains challenging, and examples of ‘on‐the‐ground’ implementation remain scarce in the literature. We present a practical case study in which scientists from the Federal Department of Environment and Climate Change Canada and Inuit have co‐delivered a multidecade‐long monitoring program of nesting common eider ducks Somateria mollissima in the Arctic. We review our experience as southern‐based government researchers in this collaboration. We reflect on successes and, more importantly, on the practical challenges that prevent the full implementation of best practices in our program. First, we highlight challenges to co‐designing a data collection protocol that combines both Indigenous and Western scientific methods. We show how combining the strengths of Inuit Knowledge and rigorous random sampling design has led to a more powerful approach to eider population monitoring. Second, we review how the federal government's administrative approaches are poorly suited for employing seasonal Indigenous workers living in remote communities, particularly in Canada. We argue that to deliver respectful employment and payment practices, the financial and hiring administration of collaborative projects must be based at the community level. Finally, we show how sociocultural factors have made it challenging to ensure the safety of all field workers consistently. To increase their perceived value and uptake, we suggest that safety guidelines must be co‐designed by visiting researchers and local partners for each project to ensure that they are appropriate to the local culture, field conditions, and the nature of the fieldwork. Based on our experience, we draw attention to gaps that still exist between the best practices of collaborative research and factors that hamper their practical implementation. We invite other research teams to do the same so that, collectively, we can improve collaborative approaches nationally and internationally.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.040 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".