The government-led climate change adaptation landscape in Nunavut, Canada
Bibliographic record
Abstract
The Canadian Arctic is uniquely sensitive to climate change impacts, including rapidly warming temperatures, sea ice change, and permafrost degradation. Adaptation—including efforts to manage climate change risks, reduce damages, and take advantage of new opportunities—has been identified as a priority for policy action across government levels. However, our understanding of adaptation in the Canadian North is limited: Is adaptation taking place, to what stresses, and what does it look like? In this paper we answer these questions for the Inuit territory of Nunavut, systematically cataloguing and reviewing government-led adaptation programs and policies at community, territorial, and federal levels, drawing on publically available information. We documented a total of 700 discrete adaptation initiatives. The focus on adaptation to-date has primarily been at the groundwork level, aimed at informing and preparing for adaptation through impact assessments, adaptation planning exercises, and stakeholder engagement. Adaptation in Nunavut has been driven by cross-scale coordination and leadership from the territorial and federal government. Our study finds few examples of concrete actions for planned adaptation, such as changes to or creation of policies that enable adaptation, alterations to building codes and infrastructure design with changing geo-hazards, or enhanced disaster planning and emergency preparedness in light of projected impacts. This study indicates a need for formal adaptation plans for the Governments of Canada and Nunavut, emphasis on adaptation monitoring and evaluation, and a greater role of Inuit traditional knowledge and cultural values in adaptation policy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".