The Intersection of Incarceration and Injustice: Environmental Burdens in Prison Communities
Bibliographic record
Abstract
Background: This study examines environmental justice (EJ) indicators in communities surrounding 165 prisons in 10 U.S. states, contributing to timely and critical discussions of both decarceration and EJ in smaller towns and rural areas of the United States. Methods: Environmental Protection Agency's Environmental Justice Screening and Mapping Tool (EJSCREEN) was used to characterize environmental burdens in communities surrounding state and federal prisons. Based on findings, brief case studies of five prison communities with multiple EJ concerns are presented. Results: Communities surrounding 40% of the prisons exceeded an 80th percentile threshold EJ Index for one indicator; nearly one-quarter exceeded this threshold for multiple EJ Indexes. The prisons tended to be in less-densely populated areas; only 4% of prisons in these 10 states were in cities. States with higher incarceration rates tended to have a greater number of elevated EJ Indexes for communities surrounding prisons. Discussion: Findings support the existence of many rural EJ communities, and a multitude of pollution sources may contribute to environmental conditions in communities surrounding prisons. Although EJ concerns impact a broad set of stakeholders, prison inmates represent a unique population: involuntary subjects of environmental burdens they are unable to escape during the period of their incarceration. Study findings are also discussed in the context of proposed actions under the Biden Administration's Justice40 Initiative. Conclusion: Intersectional approaches are needed to understand and solve complex problems. This study finds that rural communities, increasingly the sites of prisons, present EJ concerns worthy of further examination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".