Engaging Northern Indigenous Communities in Biophysical Research: Pitfalls and Successful Approaches
Bibliographic record
Abstract
Guidelines and best practices to engage Indigenous people in Arctic regions in biophysical research have emerged since the 1990s. Despite these guidelines, mainstream scientists still struggle to create effective working relationships with Indigenous people and engage them in their research. We encountered this issue when we visited three communities on Alaska’s west coast to study impactful weather events and the formation of “slush ice berms,” which can protect towns from storm surges. As we worked to build relationships with residents of the towns, we found the existing guidelines are often helpful for telling us what to do—for example, they emphasize the importance of face-to-face communication—but researchers also need to think about how to do it (skills) and how to be (personal attributes). To demonstrate to Indigenous people that we value and respect their culture, researchers could learn to use language that is understandable and that reflects a collaborative rather than a top-down approach. We should be ready to adjust our schedules and to help the community we are visiting, rather than simply focusing on our own needs. We might look for benefits for the community and ensure residents understand and are satisfied with the research we are doing. Some of the necessary attributes we identified are curiosity, honesty, interpersonal awareness, empathy, flexibility, and openness. Although the skills and attributes presented here are useful to bridge the gap between cultures, we caution that there is no specific formula that can guarantee success.
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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 | 0.310 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.032 | 0.054 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.012 | 0.034 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".