Cracking Appalachia: A Political-Industrial Ecology Perspective
Bibliographic record
Abstract
This paper presents a political-industrial ecology (PIE) analysis of a petrochemical ethane cracker plant located above the Marcellus Shale Basin near Pittsburgh, Pennsylvania. The analysis is motivated by community concerns that the cracker is more than just a plant and that current regulatory practices render the broader petrochemical ecosystem within which the plant exists largely unknowable. By integrating theory and methods from urban political ecology and Vienna School social metabolism, I present a metabolic tour of the petrochemical ecosystem to better render it visible and to situate it within the evolving global petrochemical economy. Within Pennsylvania, the plant exists in an ecosystem of over 20,000 energy infrastructures whose exact numbers and locations are largely unknown due to regulatory practices and exemptions unique to the energy industry. Because of this infrastructure buildout, the Marcellus Shale Basin is now interconnected to the US Gulf Coast, Canada, and Europe, resulting in more globally integrated, separate markets for natural gas and petrochemicals. As reconceptualized through PIE, this paper demonstrates how metabolism, a resurgent concept within various social and engineering science disciplines, can be a method for advancing community-engaged research by simultaneously embedding industrial ecosystems within place and assessing their broader socioecological significance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".