Learning from the past to deal with the future: Using different knowledges to ensure food security in the Tsá Tué biosphere reserve (Northwest Territories, Canada)
Bibliographic record
Abstract
The community of Délı̨nę, located in the UNESCO Tsá Tué Biosphere Reserve, is experiencing the impacts of climate change on the lands surrounding Great Bear Lake, in Northwest Territories, Canada. These impacts are limiting the community's ability to access the land to support their food system, which depends on harvesting traditional foods. This article details a participatory action research approach, driven by the community, that used on-the-land activities, workshops, community meetings and interviews to develop a community food security action plan to deal with the uncertainties of a changing climate on the food system. Data was analyzed using the Community Capitals Framework (CCF) to describe the complex nature of the community's food system in terms of available or depleting capitals, as well as how the impacts of climate change affect these capitals, and the needs identified by the community to aid in adaptation. For Délı̨nę, the theme of self-sufficiency emerged out of concerns that climate change is negatively impacting supplies from the south and that building and maintaining both social and cultural capital are key to achieving food security in an uncertain future. Learning from the past and sharing Traditional Knowledge 1 was a key element of food security planning. However, other types of knowledge, such as research and monitoring of the health of the land, and building capacity of the community through training, were important aspects of adaptation planning in the community. This knowledge, in its many forms, may assist the community in determining its own direction for achieving food security, and offers a glimpse into food sovereignty in Northern regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".