When the river started underneath the land: social constructions of a ‘severe’ weather event in Pangnirtung, Nunavut, Canada
Bibliographic record
Abstract
ABSTRACT In June 2008, the community of Pangnirtung, Nunavut, Canada experienced a rainstorm that caused structural damage to the community's bridge and extensive permafrost erosion along the Duval River. The local government characterised the event as ‘severe’ and focused their attention on the bridge collapse, in contrast to the residents, who described this particular consequence as inconvenient at worst and at best, exciting. Instead residents expressed greater concern for the permafrost erosion and the uncertainty this posed for community well-being. This article follows an 11 week anthropological field trip to Pangnirtung in the summer of 2009 and is based on 31 semi-structured interviews, two focus group discussions, and participant observation. We explore how social processes influence subjective constructions of what constitutes ‘severe’ weather in the community, and attempt to explain how such constructions lead to differing perceptions of vulnerability to ‘severe’ weather events. Contributing factors including the normalisation of threat, local beliefs regarding change and uncertainty, as well as the communication of risk information are discussed along with the different coping strategies used by government and residents in managing their perceived levels of vulnerability. The research shows the importance of understanding the role social processes play in shaping local conceptions of ‘severe’ and perceptions of vulnerability to ‘severe’ weather events. This study enhances understandings of difference within populations and adds to the growing body of literature that demonstrates the need to incorporate locally relevant indices when conducting vulnerability assessment.
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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 | 0.002 | 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 | 0.029 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".