Using Indigenous and Western knowledge systems for environmental risk assessment
Bibliographic record
Abstract
Indigenous rights, knowledge, and value systems are linked inextricably to the lands, waters, and non-human beings that form the environments of Indigenous Peoples. Across the globe, the rights of Indigenous peoples are being formally recognized and as a result, efforts are being made to include Indigenous Knowledge and value systems in environmental policy and decision making. Scientists and decision makers must not only recognize this reality, but also operationalize these efforts through meaningful changes to create space for the inclusion of Indigenous Knowledge, Indigenous values, and sovereignty within the current methods for scientific enquiry and the development of environmental policies. Professionals in the environmental field have a responsibility to ensure that their work has a positive impact on Indigenous Peoples and their environments. In this study, we explore the concept of consultation and informed consent through the lens of the development of environmental policy and decision making. We will discuss these concepts in the context of ecological risk assessment related to a case study focused on contaminated sediment in a harbor within the Great Lakes. We will demonstrate a process that deconstructs the current protocols for risk assessments at sites with localized pollutants in sediment and rebuilds them with elements that recognize both Western and Indigenous knowledge systems. This process includes collaborative fieldwork, relationship building, and informal and formal interviews with participants and community members. By utilizing such approaches, we were able to develop a risk assessment framework that recognizes the sovereignty of Indigenous peoples and promotes effective Nation-to-Nation decision making.
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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 | 0.044 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".