Culturally Driven Freshwater and Fish Monitoring: Opportunities for Social-Ecological Learning in the Northwest Territories’ Dehcho Region
Bibliographic record
Abstract
There is growing concern about the sustainability of freshwater ecosystems in northern Canada that are under significant stress from climate change, resource development, and hydroelectric development, among others. Community-based monitoring (CBM) based on traditional ecological knowledge (TEK) has the potential to contribute to understanding impacts on the environment and community livelihoods. This thesis shares insights about culturally driven monitoring, through collaborative research with Kátł’odeeche First Nation (KFN) in the Northwest Territories. This research was initiated in 2018 to improve understanding of the changes occurring in the Hay River and Buffalo River sub-basins, which extend primarily across the Alberta and Northwest Territories borders. Drawing on 15 semi-structured interviews conducted with KFN elders, fish harvesters, and youth, this research illustrates the kinds of social–ecological indicators used by KFN to track changes in the health of aquatic systems as well as the fishing livelihoods of local people. Utilizing indicators, fishers observe declines in fish health, water quality, water quantity, and ice thickness in their lifetime. Community members perceive these changes to be a result of the cumulative effects of environmental stressors. The indicators as well as trends and patterns being observed and experienced can contribute to both social-ecological learning in the community as well as the governance of the larger Mackenzie River Basin. In addition, this research generates outcomes about knowledge sharing, social-ecological learning and CBM based on interviews with KFN and six government and academia actors. Overall, findings increase understandings of the content, mechanisms and flows (relationships) by which knowledge is shared in the context of freshwater and fish monitoring within KFN and among various actors, including Indigenous communities, territorial and federal governments. Evidently, CBM provides opportunities for social-ecological learning, which is necessary in improving the management of the Hay River and Buffalo River sub-basins and the larger Mackenzie River Basin, as it contributes to increased understanding of environmental change. This research highlights the importance of relationships among actors in promoting knowledge sharing and uncovers various barriers that may hinder opportunities for social-ecological learning and knowledge sharing through CBM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".