Environmental Change and Adaptive Capacity in Arctic Communities: an Integrative Approach
Bibliographic record
Abstract
This paper illustrates integration of natural and social science data with the knowledge from community members to document how climate change is being experienced and how policy decisions can enhance capacity to adapt in the future. Consistent with the approaches of Lim et al., 2001, Lange, 2003, and Turner et al., 2003, the vulnerability model provides a robust structure to connect and integrate research findings. The framework has four interconnected components: · What are the environmental conditions (exposures) that are relevant to people and communities and how are they experienced? · What are the adaptive strategies and management systems used to deal with exposures and in what ways are they successful? · What future trends or changes can be expected in environmental conditions that relate to people and communities? · What is the capacity to deal with those changes, and what adaptive measures or policy changes can be taken to enhance that capacity? These questions can be used to direct integrated research programs, but here they provide a means of structuring research findings, some of whichhave been compiled independently. Examples of research in Nunavut and the Inuvialuit Settlement Region (ISR) show how community insights (including traditional knowledge) and scientific knowledge (physical, biological, social and health) are used to assess exposures and to identify practical opportunities for adapting. For example, in the community of Ulukhaktok travel to spring and summer harvesting areas are affected by changes in the timing of sea ice freeze-up and break-up. These changes are consistent with documented temperature increases and records of sea ice dynamics in the Western Arctic during the last half century (McBean, 2005). Harvesters are adapting by traveling to alternative harvesting areas using alternative modes of transportation. Sea ice models predict a continued reduction in sea ice extent which will have further implications for travel and harvesting (Arzel et al., 2006). Hunters have shifted target species, with more harvesting of terrestrial wildlife, which has implications for wildlife management. Another policy connection is the Inuvialuit Harvesters Assistance Program (IHAP) which provides financial assistance to Inuvialuit harvesters to purchase alternative equipment to harvest under the changing ice and land conditions. Policy decisions to address climate change effects are invariably made relevant to multiple stresses that are already pressing. This framework provides a structureto integrate natural science findings with social science, to incorporate traditional knowledge, and to directly connect science to decision-making and policy. It serves the central objective of ArcticNet and can be applied to integrate within regions (IRIS) and to synthesize across regions for an assessment of the 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.014 |
| 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".