The Relationship between Climate Vulnerability and Disaster Declarations: A Case Study of Flood-Prone Indigenous Communities in Alaska
Bibliographic record
Abstract
Using descriptive statistics, correlation analyses, one-way ANOVA, and an environmental justice lens, we considered the social and physical vulnerability factors associated with climate change and flood-related disaster declarations for Alaska Native Villages (ANVs), which are indigenous communities. We found that, on average, compared with communities not involved in disaster declarations, communities with disaster declarations between 1977 and 2014 had lower elevation, more exposure to erosion, less precipitation in summer and fall, larger populations, less population growth, a higher percentage of youth, larger household size, a higher percentage of Alaska Natives, more population below the poverty level, and lower per capita income. This finding is positive, because it suggests that, collectively, communities with greater vulnerability are getting more disaster declarations, which come with disaster aid. However, some of the most vulnerable communities may not be getting all the help they need. In particular, southwestern communities along the Yukon and Kuskokwim Rivers were statistically more likely to get disaster declarations than were ANVs that received attention in the media and the literature for their 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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".