ORIGINAL ARTICLE Climate change adaptation planning in remote, resource-dependent communities: an Arctic example
Bibliographic record
Abstract
Abstract This paper develops a methodology for climate change adaptation planning in remote, resource-dependent communities. The methods are structured using a vulnera-bility framework, and community members, local stake-holders and researchers are engaged in an iterative planning process to identify, describe, prioritize and pilot adaptation actions. The methods include: (1) analysis of secondary sources of information, (2) community collaboration and partnership building, (3) adaptation planning workshops, (4) adaptation plan development, (5) key informant and com-munity review and (6) pilot adaptation actions. Vulnerability to climate change is assessed in the context of other non-climatic factors—social, political, economic and environ-mental, already being experienced in communities and which influence how climate change is experienced and responded to. Key exposure-sensitivities and related adap-tation options are identified in five sectors of a community: business and economy, culture and learning, health and well-being, subsistence harvesting, and transportation and infra-structure. This organization allows for focused discussions and the involvement of relevant stakeholders and experts from each sector. The methodology is applied in Paulatuk, an Inuit community located in the Inuvialuit Settlement Region (ISR), Northwest Territories (NWT), Canada, and key findings are highlighted. The methods developed have important lessons for adaptation planning in remote, resource-dependent communities generally and contributes to a small but growing scholarship on methodology in the human dimensions of climate change.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".