Political ecology of climate change adaptation in the Arctic: Insights from Nunatsiavut, Canada
Bibliographic record
Abstract
Political ecology analyses climate change adaptation by examining the intricate relationships between systemic inequalities, power dynamics, and structural factors, including colonialism and capitalism. This paper examines the political ecology of climate change adaptation in the Arctic, focusing on five Inuit communities in Nunatsiavut, a self-governing Inuit region in northern Canada. It examines how various social, economic, and environmental factors intersect to influence adaptation. We found that colonialism, forced relocation, and capitalism are driving the historical construction of climate risk along with contemporary adaptation challenges, and showcase how inequities affect the ways different community members experience and respond to climate change. Inuit communities face significant adaptation barriers, such as high costs associated with store-bought food and machinery, economic constraints, and technological dependence required for food gathering. Using a political ecology lens, we contextualised these barriers within the broader socioeconomic factors. The analysis centres on the critical question of "adaptation for whom?" and examines the barriers and limits to adaptation, emphasising the uneven distribution of adaptive capacity within Nunatsiavut. This study underscores the need for an equitable approach to adaptation that addresses the systemic, structural, and infrastructural challenges faced by Inuit in a rapidly changing Arctic. This research was conducted in accordance with Indigenous and Inuit research ethics, ensuring Inuit self-determination and community control over the research process.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 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) | 0.003 | 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".