Chapter 1. A Poison Runs Through It: The Elk River Chemical Spill in West Virginia
Bibliographic record
Abstract
In our culture we tend to view disasters as isolated, exceptional events.We need to instead view them as connected to one another along various social fault lines and as a direct product of socioeconomic processes that transcend traditional boundaries of time and space.By placing disasters back into the dynamic fi eld of social processes and translocal boundaries, we gain a greater understanding of their origins.Like the accounts of chemical contamination in other chemical corridors around the nation, such as the notorious Cancer Alley in Louisiana, the strip along the Gulf Coast of Texas, the chemical corridor in New England (a legacy of the Industrial Revolution), the chemical corridor in western New York and Ontario, Canada (stretching originally from the Niagara Falls area to the Great Lakes on both sides of the international border), as well as in other areas of the country including Silicon Valley, the Elk River chemical spill was not an isolated event bound by space and time.Rather, it was the manifestation of historical processes shaped by economic and political forces from as far away as India, France, Germany, Washington, DC, Tennessee, Michigan, and Atlanta, Georgia ( Button).Thus, the spill in the Elk River serves as a classic example of how disasters are unfolding processes contextualized deep into the past and richly confi gured in the present.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".