Operationalizing longitudinal approaches to climate change vulnerability assessment in the Arctic
Bibliographic record
Abstract
The last decade has seen a proliferation of community-scale climate change vulnerability assessments globally, and specifically in the Arctic. Many of these have employed a vulnerability framework, drawing upon interviews with community members to identify and characterize climatic risks and adaptive responses. This has led to the development of useful baseline understandings of vulnerability and adaptation. However, these understandings aretemporally static; because vulnerability and adaptation are dynamic processes, new methodologies are needed to generate insights on the dynamics of how climate change is experienced and responded to by communities and individuals. The use of longitudinal approaches to capture the dynamism of human processes is wellestablished in sociology and the health sciences, but the uptake of such approaches remains limited in climate change vulnerability research. Therefore, we propose the application of two longitudinal approaches – cohort and trend studies – in climate change vulnerability assessment and review three case studies from ArcticNet research in the Canadian Arctic. These case studies offer an example of how longitudinal approaches can be operationalized in vulnerability research in the Arctic, and globally, to capture the dynamism of vulnerability through the identification of climatic anomalies and trends, the temporal development of adaptive pathways and the effects of interactions and convergences between conditions, and insights on themes critical to understanding adaptation such as social learning and knowledge sharing. This research is part of ArcticNet Project 1.1 Community Vulnerability, Adaptation and Resilience to Climate Change in the 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".