Policy, plans and processes for developing and improving the use of hazard maps in climate change adaptation for Yukon communities
Bibliographic record
Abstract
Climate change has quickly become one of the most commonly discussed topics in northern Canada. Communities located in Canada’s north are facing impacts to every aspect of life. Hazard mapping is one of the tools used to predict risk from climate change impacts and thus assist communities in adaptation planning. The Northern Climate ExChange in Yukon has developed hazard maps for seven communities across Yukon. While hazard maps are a useful tool, in order for them to reach their maximum potential, users need to be well educated and trained to use them. Capacity and communication are arguably the most significant challenge faced by northern communities when it comes to climate change adaptation.\nThe goal of this research is to connect hazard mapping research to community needs, and opportunities for adaptation and mitigation to assist with both development and adaptation to future changes to the land and ecosystems. This research is focused on case-studies of Burwash Landing & Destruction Bay and Old Crow, Yukon. These two communities were chosen because they have an existing hazard map and contrasting community characteristics, meaning the research has validity with a wider range of communities. Interviews were conducted in both communities and analyzed for common themes and responses. The interview questions were designed with the intention to stir discussions around climate change issues in the communities, general discussions around hazard maps and recommendations for governments, organizations and communities. Through interview analysis, six recommendations emerged, related to how research was conducted, communication pathways, education and government, research organization, and institutional structures.\nRecommendations for research are to incorporate GIS map layers into existing mapping programs, and refine the language used in reports and outputs to best reflect community needs. The recommendation for communication pathways is to use stories to communicate successes and failures. The recommendation for education is to develop avenues to provide continued, consistent education in the communities. Lastly, recommendations for governance structures are to develop a climate change adaptation strategy framework, and for higher governing bodies to push forward in support of community-based research.\nThis research highlighted many common issues for conducting research in the north. Overwhelmingly, research outputs, communication barriers, and education were consistent themes. As climate change continues to alter northern communities, planning for these changes will become crucial for long-term survival and community resiliency.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".