Community-Based Environmental Monitoring (CBEM) for Meaningful Incorporation of Indigenous and Local Knowledge Within the Context of the Canadian Northern Corridor Program
Bibliographic record
Abstract
Meaningful incorporation of Indigenous and local knowledge (ILK) in climate change mitigation and adaptation efforts is key to accelerating effective action plans. This study argues that community-based environmental monitoring (CBEM), if done properly, can be more effective in incorporating ILK than environmental impact and monitoring based only on Western science. The paper examines successful elements, benefits, challenges and limitations in the existing CBEM studies that incorporate ILK to recognize how to design comprehensive CBEM policy for large-scale infrastructure projects such as the Canadian Northern Corridor (CNC) concept. Based on a proposed framework for CBEM implementation (CBEM-IF), the study examines three Canadian CBEM case studies: berry pollution monitoring (AB), water quality monitoring (AB, BC, NWT, NT, SK and YT) and caribou monitoring (QC and NL), to evaluate lessons learned and to inform future CNC policy development. This study illustrates how knowledge co-production provides more opportunities for actions in sustainable development and incorporates emotional and spiritual components that entail different conceptualizations of human-nature connectedness. CBEM facilitates the incorporation of ILK and science, engages community members in the monitoring process and produces research outcomes which stakeholders perceive as more legitimate and relevant. CBEM can be a powerful tool in land-use conflict resolution, and it represents an inexpensive approach to monitoring the Arctic and near-North. Indigenous leadership, technology incorporation and equal partnership with communities, and availability of institutional guidelines were identified as required to enable the proper implementation of CBEM programs within the CNC. However, certain limitations of CBEM include lack of policy and guidelines; high reliance on volunteers; lack of standardized methods; focus on specific types of a landscape; general issues with TEK incorporation into science; and policy issues due to the incommensurabilityof Western science and the ILK epistemologies. Such challenges can be generalized as technical, organizational, financial and environmental issues and can be addressed by applying successful elements from previous international and Canadian CBEM studies. The authors suggest a series of policy recommendations to enable the implementation of CBEM as a means for meaningful incorporation of ILK on sustainable development projects and the CNC.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".