Prioritizing Climate Change Adaptations in Canadian Arctic Communities
Bibliographic record
Abstract
Arctic regions are experiencing the most rapid climate change globally and adaptation has been identified as a priority across scales. Anticipatory planning to adapt to the impacts of climate change usually follows a number of steps: assess current and future vulnerability, identify potential adaptations, prioritize options, implement prioritized options, and monitor and evaluate implementation. While most of these steps are well documented, there has been limited examination of the process of adaptation prioritization in Arctic communities. In this paper, we build upon existing tools and propose a framework for prioritizing adaptation options and guiding decision-making for implementation in Arctic regions. Using four adaptation performance criteria (timescale, equity, sustainability and total costs) to evaluate options through a multi-criteria decision analysis coupled with a network centric approach, our Adaptation Prioritization Framework promotes a participatory approach for adaptation prioritization and planning. We illustrate application of the framework using a hypothetical example from the territory of Nunavut in the Canadian Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".