This isn't something that's Merritt's fault: Understanding risk perceptions and responses to place-based risk in the Nicola Valley, British Columbia
Bibliographic record
Abstract
It is clear that climate change is unfolding in communities across the world, manifesting in extreme weather and culminating in natural disasters. What remains unclear, however, is how climate-related extreme weather impacts the bonds that people form with places, and how these bonds influence the process of climate adaptation. This thesis takes up these questions, centering risk perceptions and people-place bonds in an examination of the experiences of Nicola Valley residents following wildfires and flooding in 2021. Based on semi-structured interviews with residents about their experiences and perspectives on climate-linked extreme weather and disaster, this thesis offers a place-sensitive understanding of how individuals interpret and respond to extreme weather risk. The influence of strong place attachment offers a nuanced explanation for why individuals may actively choose to remain in locations with known risks. I suggest that this occurs through the social process of risk dis-placement, which locates the source of risk as being external to one’s valued place. Place attachment also has implications for communities and their social relations. In the Nicola Valley, place attachment facilitates community cohesion, yet it also can exacerbate division between insiders and outsiders. I highlight the necessity of considering place as a lens through which risk is perceived and understood; the absence of this lens overlooks one of the key underlying factors in individuals’ risk perceptions and responses. Therefore, I maintain that successful efforts at adapting to climate change are those that account for relationships between places, individuals, and 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".