Pigs, waste, and creative extraction: racialised uneven development in Eastern North Carolina
Bibliographic record
Abstract
Since the 1990s, North Carolina pig farms have generated ten billion gallons of waste annually that disproportionately impact racialised, Indigenous, and low-income communities. This article examines two developmental phases of the hog industry and its waste management regimes to trace the processes through which agribusiness and state collude, perpetuating racialised uneven development in Eastern North Carolina. The first phase (1980–2007) framed industrial hog production as essential for economic growth, shifting to industrial-scale production and waste management practices. In response to environmental and social crises, the state introduced the 1997 moratorium that mandated the adoption of alternative waste management systems. The second phase (2007-present) reframes waste management as a strategy for climate change mitigation. This phase is marked by the passage of two interlocking bills that carved out a pathway for growth through waste, circumventing the adoption of alternative waste management systems. Through qualitative research, including interviews, participant observation, and document analyses, we examine how state-agribusiness relationships undermine community-led efforts for environmental and climate justice through the development framework “creative extraction” as theorised by Purifoy and Seamster (2021). Our article reframes industrial animal waste not just as pollution but as a critical site where the hog industry, with unwavering state support, has entrenched and expanded its production. The emergence of directed anaerobic digesters exemplifies not only greenwashing but also the collusive dynamics between the state and agribusiness, highlighting the need to foreground these relationships in efforts to develop agricultural and energy pathways defined by justice and self-determination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".