Living with climate change: community based vulnerability research
Bibliographic record
Abstract
Climate change is already being experienced in the Arctic with implications for people’s livelihoods and lives yet research on climate change has often taken the form of large scale and long term impact studies on physical and biological systems. Already questions are being raised about the degree to which adaptations can be stretched to deal with changing conditions. Climate change is global in scale but research has shown that the impacts of climate change have and will be felt strongly at the local level. A proven method for understanding the implications of climate change on people and their resource use is to assess community vulnerability. When the purpose of the research is to assess the vulnerability of a community we need to look at both the physical conditions that create risks for the community and also at the community’ s ability to cope with, recover from, or adapt to external risks. Vulnerability is not exclusive to physical stresses and effects but must be considered within the scope of other factors including local geography, community history, economy, culture and social conditions. Vulnerability assessments need to focus on and engage with community members in order to identify conditions that are relevant to the community and adaptations that are realistic. It is through a collaborative process involving the integration of western science and local (or traditional) knowledge, including in the design of the research project itself that one can gain insight into the actual implications that climate change has on a community and to what capacity that community has to deal with current and potential climate related risks. This poster outlines a method for assessing community vulnerability and builds upon examples of research on vulnerability and adaptation in the community of Ulukhaktok (Holman, NT) in the Western Canadian Arctic.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".