Towards more inclusive and solution orientated community-based environmental monitoring
Bibliographic record
Abstract
Abstract Rapid climate-driven environmental change continues to threaten front-line communities that rely on Arctic landscapes to sustain their way of life. Community-Based Monitoring (CBM) can increase our knowledge of environmental change and understanding of human-environment interactions occurring across the Arctic. However, the depth of CBM research outcomes have been limited by an imbalance in contributions from external researchers and community members. A detailed literature analysis revealed that the number of studies documenting CBM approaches in Inuit Nunangat (Inuit homeland in Canada) have increased over the last decade. We identify that bottom-up guiding protocols including the National Inuit Strategy on Research, has increased community engagement in Arctic research processes and equitable outcomes. However, these increases have been concentrated on wildlife-based research where consistent funding streams and pre-existing alignment with community priorities exist. To explore the potential for guiding principles to be more successfully incorporated into impactful CBM, we present a co-developed environmental CBM case study aiming to document and aid understanding of climate-driven landscape change near Tuktoyaktuk, Inuvialuit Settlement Region, Canada since 2018. A foundation of early dialogue and collaborative partnerships between community members and external researchers formed the basis of a community-based climate monitoring program driven by community research priorities. A succession of funded CBM projects at Tuktoyaktuk demonstrated that longer term and resilient climate monitoring can bring together Scientific and Indigenous knowledge systems. Progressing beyond an emphasis on data collection is vital to sustain monitoring efforts, capacity sharing and co-dissemination processes to ensure research is communicated back in a way that is understandable, relevant, and usable to address community priorities. The need for successful CBM is often at odds with current research funding structures, which risks a fragmented mosaic of early-stage initiatives focused on understanding environmental problems rather than sustained and progressive research development towards cooperative solutions.
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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 | 0.002 | 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 | 0.012 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".