“The River is Not the Same Anymore": Environmental Risk and Uncertainty in the Aftermath of the High River, Alberta Flood
Bibliographic record
Abstract
Even when individuals are aware of and well educated about environmental issues like climate change they often take little action to mitigate these problems. Yet catastrophic events, like disasters, have the potential to rupture or disrupt complacency toward environmental problems, forcing individuals to consider the potential effects of human activity on the environment as they expose how environmentally harmful practices put people at risk. This article is based on focus group interviews with 46 residents of High River, Alberta, a rural community hardest hit by the 2013 Southern Alberta flood. It examines if and how experiencing the flood prompted residents to think about the environment or interact with it in new ways. Findings suggest that residents voice a contradiction- while they believe that pre-flood human activity like deforestation, river diversion, and home-building altered the environment and placed communities like their own at risk, they also argue that natural forces like disasters are immune to human efforts to control them. Residents feel their environment is less stable and predicable since the flood, and they worry more about toxicity and associated environmental health risks. The article concludes with a discussion of the implications of these findings for environmental sociology and public policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".