Wrong side of the tracks: the neglected human costs of transporting oil and gas.
Bibliographic record
Abstract
The connection between human rights and climate change is most evident when examining carbon dioxide emissions that result from burning fossil fuels (e.g., sea level rise and displaced coastal cultures). However, the transport of fossil fuels also has human rights implications for human rights and climate change. This research focuses on the health and safety risks inflicted on those residents who are adjacent to the railways that ship fossil fuels along the US-Canada transportation corridors. Applying sociological and jurisprudential perspectives, we review the environmental/climate justice literature as it pertains to industrial transport corridors, documenting the forms of heightened risk imposed on those living along these transportation paths. Next, we develop an illustrative case study of Canada's worst rail catastrophe. In so doing, we provide evidence of a decades-long failure of US and Canadian regulators to prevent such disasters. We interpret that disaster through a human rights case law suggesting that States have an affirmative duty to protect their citizens from foreseeable disasters. Based on this analysis, we propose specific rail safety regulatory reforms. We argue that if the US and Canadian governments implement these regulations as required under human rights law, they can more effectively honor their obligations to their citizens who are paying a high human cost for the material benefits associated with increased energy production.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".