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Record W4244685668 · doi:10.31235/osf.io/s4ufr

“The River is Not the Same Anymore": Environmental Risk and Uncertainty in the Aftermath of the High River, Alberta Flood

2018· preprint· en· W4244685668 on OpenAlexaboutno aff
Timothy J. Haney, Caroline McDonald‐Harker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythWorryEnvironmental planningCall to actionAction (physics)Natural disasterEnvironmental resource managementClimate changeEnvironmental ethicsGeographyPolitical scienceBusinessPsychologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.238
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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