A Community-Based Approach to Mapping Gwich'in Observations of Environmental Changes in the Lower Peel River Watershed, NT
Bibliographic record
Abstract
In Canada's western Arctic climate change is driving rapid ecological changes. Ongoing and locally-driven environmental monitoring, in which systematic observations of environmental conditions are recorded and synthesized, is required to understand and respond to climate change and other human impacts. Indigenous peoples' traditional ecological knowledge is increasingly used as the basis for regional monitoring, as there is a need for detailed, place-specific information that is consistent with local ways of understanding and interacting with the environment. In this project, participatory multimedia mapping was used with Teetł'it Gwich'in land users and youth from Fort McPherson, Northwest Territories, Canada to record information about local environmental conditions and changes. Gwich'in monitors made trips on the land to document environmental conditions and changes using geotagged photo and video observations. Subsequently, land users provided detailed information about each observation in follow-up interviews, which were added to a web-based map displaying participants' photos and videos. In this paper, we present the outcomes from the first year of research, explore the diverse types of knowledge this approach can contribute to environmental monitoring, and identify areas of convergence between traditional ecological knowledge and scientific research in the Arctic. Our work shows that this approach can make an important contribution to monitoring environmental changes associated with climate change in a way that is locally relevant and culturally appropriate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".