Indigenous Peoples and environmental research and monitoring within the Laurentian Great Lakes Basin: A systematic map
Bibliographic record
Abstract
Abstract The North American Great Lakes basin faces management challenges worsened by climate change, invasive species and contaminants. Addressing these issues requires a collective approach, drawing on a diversity of perspectives and voices, including that of the Indigenous Peoples in the region—Tribal Nations, First Nations and Métis peoples. For these priorities and needs to be meaningfully included, Indigenous communities, Nations and Tribes must be supported to participate in and direct environmental research and monitoring through approaches that respect community‐based research and learning. However, the extent of environmental research and monitoring conducted with, for or by Indigenous Peoples (including Indigenous communities, Nations and Tribes) in the Great Lakes region is not well‐documented. Without a clear understanding of what research is taking place, where and how Indigenous Peoples are involved, it is difficult to assess whether these efforts are truly inclusive or aligned with Indigenous priorities—highlighting the need to systematically map and characterize these efforts. A systematic mapping protocol was applied to identify, gather and review English‐language peer‐reviewed literature on environmental research and monitoring in the Great Lakes basin conducted with, for and by Indigenous Peoples. Sixty studies were identified and included in the systematic map database gathered and analysed for this review. The systematic map reveals that 57% of the studies took place in or around Lake Superior. Many focussed on human health risk assessments, particularly related to environmental contaminant exposure. The role of Indigenous Peoples varied, with most studies reporting involvement at the data gathering and collection stage. Practical implication . This map describes the extent, focus and roles of Indigenous Peoples in Great Lakes research and monitoring, based on peer‐reviewed English literature. If this literature reflects broader research activity, findings suggest limited representation—only 19% of Indigenous communities and Nations across the basin were included, and few were engaged in early‐stage planning or priority setting. However, this literature likely underrepresents the full scope of Indigenous‐led or co‐developed work. To support more equitable Great Lakes research, future efforts should promote broader geographic inclusion, increased Indigenous leadership across all stages and greater transparency regarding Indigenous involvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".