Community vulnerability to changing mountain snowpacks in the Robson Valley, British Columbia, Canada
Bibliographic record
Abstract
,Mountain regions are experiencing climate change with severe consequences for ecosystems and the human communities that depend on them, necessitating place-based adaptation. This thesis examines community vulnerability to changing mountain snowpack through a mixed-methods case study of McBride and Dunster in the upper Robson Valley, British Columbia, Canada. This thesis is distinct from other mountain climate change studies in that it explores the interactions among multiple environmental and societal forces that influence sensitivity to environmental changes and the capacity to adapt. Local lived experiences were gathered through one focus group and 32 semi-structured interviews with a total of 37 residents and analyzed alongside community documents and plans, local news, and quantitative data on snowpack and streamflow. Latent content analysis revealed that residents are sensitive to decreases in mountain snowpack due to their reliance on melt run-off for freshwater. Low water availability has impacted food security, wildfire suppression, and human health and well-being. Local capacity to adapt has been undermined by the centralization of government services and resulting exodus of residents, and their knowledge and skills. Despite a long history of coping with fluctuations in weather, recent changes, including low precipitation years related to the Southern El Niño Oscillation and heat waves, are considered by many residents to be outside tolerable ranges. Supporting adaptation is rooted in increasing local social capital and cohesion by re-directing financial and human resources, and decision-making power back to northern communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".