Inequity Behind Levees, The Case of the United States of America
Bibliographic record
Abstract
Infrastructure equity is an immediate concern with levees, constituting the backbone of the U.S. protection against flooding. Flooding patterns are exacerbated by anthropogenic climate change in several regions, posing a significant risk to the economy, safety, and well-being of the nation. The evolving risk of flooding is shown to disproportionately affect historically underserved and socially vulnerable communities (HUSVCs). Here we compare the sociodemographic and socioeconomic composition of leveed and non-leveed U.S. communities and show a substantial overrepresentation of HUSVCs in leveed areas at the state, regional, and national levels. Further, we analyze the proportion of communities designated as “disadvantaged” in leveed versus non-leveed areas, revealing a substantially larger population of disadvantaged communities residing behind levees. Our analyses show that nationally, Hispanic are the most overrepresented population in leveed areas yielding a disparity percentage of 39.9%, followed by Native American (18.7%), Asian (17.7%), and Black (16.1%) communities. Communities characterized by low education, poverty, and disability exhibit a disproportionately higher presentation of 27.8%, 20.4%, and 5.4% in leveed areas across the U.S. In 43 states, disadvantaged communities are overrepresented behind levees, with a national disparity percentage of 40.6%. At the regional level, the highest disparity was observed in the Northeast (57.3%), followed by the West (51.3%), Southeast (38%), Midwest (29.2%), and Southwest (25%). The findings can enable decision- and policy-makers to identify hotspots within HUSVCs that need to be prioritized for enhancing the integrity and climate adaptation of their levee systems.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".