Empowering Indigenous-led contaminant monitoring through collaborative partnerships and two-way capacity sharing
Bibliographic record
Abstract
Climate-driven landscape change, legacy waste, and ongoing infrastructural investment are leading to concerns about contaminant release in Arctic Indigenous and Local communities. Sustainable development that considers threats posed by accelerating environmental change requires accessible, appropriate, and sustained environmental data to inform strategic decision-making. Collaborative partnerships and capacity sharing are necessary to promote resilient and sustainable environmental monitoring approaches; however, effective collaboration has been hindered by mismatches in priorities and timelines between communities and research programs. We outline the development and later evolution of a community-based environmental research program focused on monitoring contamination threats posed by legacy infrastructure sourced from industry, transportation, and domestic waste sites. Capacities and insights from diverse knowledge systems guided each stage of our research approach. Reflections provided by an Indigenous and non-Indigenous Early Career Researcher share insights into different aspects of the research process. We highlight how cross-cultural partnerships and capacity sharing have enabled evolving and reflexive community-based contaminant monitoring. Our approach facilitates a structural shift from collaborative monitoring with external analysis to autonomous monitoring that supports equity in research outcomes. Appropriately considered and resourced co-development at regular points of the research process has been critical to developing a complete and effective Indigenous-led research project.
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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 | 0.043 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".