Springtime Flood Risk Reduction in Rural Arctic: A Comparative Study of Interior Alaska, United States and Central Yakutia, Russia
Bibliographic record
Abstract
Every spring, riverine communities throughout the Arctic face flood risk. As the river ice begins to thaw and break up, ice jams—accumulation of chunks and sheets of ice in the river channel, force melt water and ice floes to back up for dozens of kilometers and flood vulnerable communities upstream. Via a comparative analysis between two flood-prone communities in Alaska and Yakutia (Siberia), this study examines key components of flood risk—hazards, exposure, and vulnerability, and existing practices in flood risk reduction in rural Arctic. The research sites are two rural communities—Galena (Yukon River) and Edeytsy (Lena River), which sustained major ice-jam floods in May 2013. The data was acquired through a combination of direct observations on site, review of documents and archives, focus group discussions, and surveys. Five focus groups with US and Russian representatives from disaster management agencies revealed a few similar patterns as well as significant differences in flood risk reduction strategies. The main differences included higher reliance on mechanical and short-term ice jam and flood mitigation efforts (e.g., ice-jam demolition) in the Russian Arctic, and lack of a centralized flood management model in the US. Surveys conducted among population at risk during the site visits to Edeytsy (November 2015) and Galena (March 2016) revealed higher satisfaction levels with the existing flood risk reduction efforts among Edeytsy residents. Survey respondents in Galena indicated the lack of ice jam removal and other flood prevention measures as the key drawback in the existing flood management. Historical analysis, conducted via the disaster Pressure and Release (PAR) model, revealed that springtime flood risk in both regions results from complex interactions among a series of natural processes that generate conditions of hazard, and human actions that generate conditions of communities’ exposure and vulnerability. The analysis revealed colonial heritage, top-down governance, and limited inclusion of local communities in the decision-making as the driving forces of vulnerability in both regions. Seasonal weather patterns and regional river channel morphology determine the location, severity, and duration of floods. The analysis also revealed the importance of continuous communication between all stakeholders in timely and effective flood risk management in both regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".