Crude Injustice: A Multiscalar Spatial Configuration of Oil Trains and Environmental Justice
Bibliographic record
Abstract
The increase of Bakken oil extraction and production in the United States in the last decade has sparked the expansion of crude-by-rail (CBR) transport. The accelerating need for CBR transport has not come without consequence, however, leaving derailments, oil spills, fires and explosions in the wake of its path. Public attention turned to CBR transport safety when an oil train derailment in Lac-Megantic, Quebec resulted in massive explosions and fatalities. This case made it clear that people who live in the blast zone, or in a 1-mile radius proximity to oil-carrying railway, disproportionately face the risks associated with CBR transport. Environmental justice (EJ) studies tell us that environmental harms are often felt unevenly where they are present, deeming people of color and impoverished people subject to making sacrifices for a supposed greater good. While most cases in EJ studies deal with fixed environmental threats, this study seeks to find nuance in the scalar theoretical underpinnings of EJ by zooming in and out on this mobile source of harm. By mapping and measuring oil train routes and demographic data in the city of Columbus, Ohio, this research begins on the local level of concentrated risk. The results show that impoverished people face disproportionate CBR risks in Columbus, an urban spaces linked to a national rail network within a global economy. However, just as an oil train moves between and beyond spaces of fixed threat, this research challenges single-scale perspectives of EJ, moving the framework from the local scale to the multiscalar.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".