Why is Coal Still Mined? Insights from Asbestos and the Structures of Risk Invisibilization
Bibliographic record
Abstract
The deleterious consequences of coal mining are well known, but little attention is paid to the most egregious of all: black lung disease. The 21st-century resurgence of black lung sheds light onto the persistent inability of regulations to address this problem. Despite this, voices seeking the fading out of coal mining based on health concerns are scarce. This paper examines why coal is still mined in the United States through a comparison with asbestos mining in Quebec, Canada, which ended in 2013 for similar health reasons. Drawing on archival research, I argue that the persistence of coal is the result of greater risk invisibilization. I rely on a structural approach to risk construction and highlight how macro-level factors shape the social construction of risks downstream. Three factors explain the unique trajectory of coal: labor’s cooptation of black lung activism, the bottom-up formulation of the federal regulatory framework, and the concentration of health risks within mining communities. This paper specifies conditions under which certain regulatory issues can become visible and lead to reforms, while others remain on the margins of the political agenda.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.094 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".