The Intersection of Incarceration and Injustice: Environmental Burdens in Prison Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it