Statistical Environmental Justice Assessment for a Transportation Corridor
Bibliographic record
Abstract
Executive Order 12898 requires federally sponsored transportation projects to evaluate environmental justice criteria. These federal requirements for transportation studies have proven difficult to evaluate because of the wide range of potential scenarios that can disproportionately affect sensitive populations (either low-income or minority groups). Therefore, environmental justice assessments typically have been conducted in geographically broad areas without refined resolution for the populations closest to the transportation route. This study uses geographic information systems technology with statistical methods to provide for a more refined analysis at the census block level. The study centers on a proposed commuter rail project in the Interstate 35 corridor in the eastern Kansas/Kansas City, Missouri, area. The project involves construction of five new commuter rail stations, and the study aims to ascertain whether construction and operation of the rail system would have disproportionate impacts on low-income people. Comparisons of the median incomes in census blocks in the county with the census blocks within a one-mile radius of the five proposed stations are provided as the basis for a quantitative environmental justice assessment. The environmental justice parameter of low-income level was evaluated using analysis of variance. Results of the study indicated that the mean of the median incomes in census blocks around one rail station differed significantly from the mean of median incomes in the census blocks around the other four stations, and from the blocks in the rest of the county. The study is useful for demonstrating the importance of using a quantitative method as a tool for environmental justice assessment.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".