Justice Through Health and Dignity: Lessons from the Closure of Tonawanda Coke Corporation
Bibliographic record
Abstract
As the United States energy sector transitions away from fossil fuels, the concept of a “just transition” is gaining attention, focusing on equitable outcomes for communities and labour forces impacted by decarbonization. While the health impacts of fossil fuel generation are well documented, there is an opportunity to examine the role of health in just transition from an environmental justice perspective. Through 14 semi-structured interviews, the authors examined community stakeholder accounts from the Town of Tonawanda, New York, where a coke manufacturing facility—one of the town’s main employers and sources of tax revenue—shut down. Interviews with environmental justice advocates, labour union officials, town staff, former employees, and community members revealed that inter-organizational collaborations among commonly siloed parties (e.g., environmental groups and labour unions) were the key mobilizing force not only in preparing for the aftermath of the shutdowns but in addressing the ongoing health impacts from the coal industry as well. Interviews revealed these health consequences to be the key catalyst for creating solidarity and synergy among formerly siloed groups. This study identifies health and bodies as key lenses to reveal the power relations embedded in issues of climate change and health, and as a key medium through which justice is prioritized in the energy transition. The experiences of the Town of Tonawanda lend insight into how an environmental justice–informed understanding of energy transition reveals the interconnectedness of health, equity, and justice in climate-mitigation actions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.074 | 0.055 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".