From degradation to reproduction: Environmental justice and the racialized politics of environmental quality in Detroit
Bibliographic record
Abstract
Environmental injustice is frequently understood as the uneven distribution of environmental harms, to which low-income and racialized communities are far more frequently exposed. Alternatively, this paper explores environmental injustice through an uneven politics of environmental reproduction. I argue that environmental injustice can occur via the disproportionate burden low-income racialized communities bear for reproducing environmental quality. To expand on this understanding, I offer both a theoretical framework that centres the socioecological reproduction of racial capitalism, as well as an empirical case study of the fraught history of wastewater management in Detroit. I use Detroit's wastewater system as an example of urban infrastructure that is fundamental to the reproduction of environmental quality, which in turn sustains racial capitalism. Municipal wastewater treatment renews local water quality to support (sub)urban life, as well as industrial and commercial cycles of accumulation-particularly given the need to comply with state water quality regulations. Yet the politically and economically uneven management of Detroit's wastewater system has also contributed to the production of a racialized water affordability crisis. Over several decades, ratepayers in majority-Black Detroit have taken on a disproportionate share of financing wastewater and stormwater infrastructure for the wider metropolitan region, inflating household water bills to the point of compromising residential water security. Here, environmental injustice emerges through an inequitable economic burden for reproducing water quality, ultimately in service of sustaining racialized uneven development across the metropolitan region. Bringing the socioecological reproduction of racial capitalism to bear on understandings of environmental injustice, I argue that scholars and activists alike must consider who pays for and who benefits from maintaining environmental quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".