Adaptation to Climate Change in the Context of Multiple Stressors in the Canadian Arctic
Bibliographic record
Abstract
Inuvialuit, a self-identified group of Inuit in Canada's western Arctic, are experiencing impacts from climate change in the context of multiple climatic and non-climatic stressors already affecting their lives and livelihoods. To support adaptation that enables Inuvialuit to maintain their traditional practices while improving their livelihoods, decision makers need to understand the role that multiple stressors have in influencing how Inuvialuit experience and respond to climate change. This thesis employs a vulnerability approach to understand how multiple stressors influence adaptation to climate change through a case study of Paulatuk, Northwest Territories, Canada. Data were collected using semi-structured interviews with community members (n=28), participant observation, and analysis of secondary sources. Findings indicate that Inuvialuit in Paulatuk are dealing with a wide range of climate and non-climate related stressors operating at multiple temporal and spatial scales. These include issues related to a mixed economy, environmental conditions, institutional education, changes in wildlife, housing shortages, new technologies, and addictions. This research suggests that non-climatic stressors represent strategic entry points to support adaptation and build resilience to deal with current and expected future climate risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".