Adapting to Climate Change: Social-Ecological Resilience in a Canadian Western Arctic Community
Bibliographic record
Abstract
Human adaptation remains an insufficiently studied part of the subject of climate change. This paper examines the questions of adaptation and change in terms of social-ecological resilience using lessons from a place-specific case study. The Inuvialuit people of the small community of Sachs Harbour in Canada's western Arctic have been tracking climate change throughout the 1990s. We analyze the adaptive capacity of this community to deal with climate change. Short-term responses to changes in land-based activities, which are identified as coping mechanisms, are one component of this adaptive capacity. The second component is related to cultural and ecological adaptations of the Inuvialuit for life in a highly variable and uncertain environment; these represent long-term adaptive strategies. These two types of strategies are, in fact, on a continuum in space and time. This study suggests new ways in which theory and practice can be combined by showing how societies may adapt to climate change at multiple scales. Switching species and adjusting the "where, when, and how" of hunting are examples of shorter-term responses. On the other hand, adaptations such as flexibility in seasonal hunting patterns, traditional knowledge that allows the community to diversity hunting activities, networks for sharing food and other resources, and intercommunity trade are longer-term, culturally ingrained mechanisms. Individuals, households, and the community as a whole also provide feedback on their responses to change. Newly developing co-management institutions create additional linkages for feedback across different levels, enhancing the capacity for learning and self-organization of the local inhabitants and making it possible for them to transmit community concerns to regional, national, and international levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".