Climate change policy responses for Canada’s Inuit population
Bibliographic record
Abstract
We identify and examine how policy intervention can help Canada’s Inuit population adapt to climate change. Information from across the Canadian Arctic is drawn upon to provide an integrated analysis of adaptation policy opportunities that are relevant and applicable to communities located in the ArcticNet IRIS 1 boundaries and elsewhere in the Canadian Arctic. The policy responses are based on an understanding of the determinants of vulnerability identified in ArcticNet supported research conducted with 22 Inuit communities. A consistent approach was used in each case study where vulnerability is conceptualized as a function of exposure-sensitivity to climatic risks and adaptive capacity to deal with those risks. This conceptualization focuses on the biophysical and human determinants of vulnerability and how processes and conditions operating at multiple spatial and temporal scales infl uence them. Vulnerability is assessed in the context of multiple stressors, climate and non-climate related, which influence how climate change is experienced and conditions adaptation. Case studies involved close collaboration with community members and policy makers to identify conditions to which each community is currently vulnerable, characterize the factors that shape vulnerability and how they have changed over time, identify opportunities for adaptation policy, and examine how adaptation can be mainstreamed. Fieldwork, conducted between 2006 and 2012, included over 745 semi-structured interviews, 35 focus groups/ community workshops, and over 107 interviews with policy makers at local, regional, and national levels. Based on a synthesis of findings across the case studies, realizing adaptive capacity and overcoming adaptation barriers requires policy intervention to: (1) support the teaching and transmission of environmental knowledge and land skills, (2) improve access to, and understanding of climate, weather and sea ice information, (3) review and enhance search and rescue capabilities, (4) strengthen harvester support programs, (5) ensure the fl exibility of fish and wildlife management regimes, (6) improve the ability of Inuit food systems to meet present dietary and nutritional requirements, (7) protect key infrastructure, (8) review building codes and land-use plans in light of current and expected climate change, (9) improve support for local economic development and entrepreneurial training and (10) establish ‘special hunting areas’ prohibited to commercial shipping and cruise ship tourism.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".