Unraveling the Social Construction of a Flooding Disaster: A Threaded Situation Analysis Approach
Bibliographic record
Abstract
Studies of, and solutions to, flooding have tended to focus on scientific and technical approaches to what is viewed as a “natural” disaster. A social constructivist perspective, on the other hand, argues that disasters, such as flooding, are a consequence of decisions and activities that impact nature; therefore, understanding and changing social practices is critical to reducing risk. We conducted a case study of the social construction of flooding in the Town of High River, the community most impacted by the 2013 floods in the province of Alberta, Canada. We examine three situations that exacerbated High River's vulnerability to flooding: (a) lack of legislative changes (b) insufficient updating of flood hazard maps, and (c) absence of flood risk notification on land titles. We analyze these situations through the recently developed threaded situation analysis (TSA) approach, demonstrating that it allows for a more comprehensive analysis than similar analytical frameworks. As part of this analysis we examine why certain social practices languish or are suppressed while others become dominant and capture actors’ willful attempts to influence practices. Although numerous scholars have critiqued centralized (top-down) approaches to flood risk governance (FRG), our article contributes to the disaster scholarship by unraveling the social construction of the 2013 Alberta flooding disaster and providing evidence of how decentralized (bottom-up) practices can impede changes that are critical for reducing flooding vulnerability.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".