Regulating Improvement: Industrial Water Pollution, White Settler Authority, and Capitalist Reproduction in the St. Clair–Detroit River Corridor, 1945–1972
Bibliographic record
Abstract
This article explores the postwar racialization of socionatural metabolisms as Michigan consolidated its capacities to regulate water pollution in the St. Clair–Detroit River corridor. These unceded waters flow through the traditional territories of the Ojibwe, Odawa, Potawatomi, Mississauga, and Wyandot nations, as well as the heavily industrialized, urbanized, and racially segregated geographies of southeast Michigan. Drawing on archival records, I examine discursive constructions of White settler and Indigenous water metabolisms that coarticulated with Michigan’s growing concern that unchecked water pollution posed a metabolic barrier to industrial manufacturing. I situate these representations against the state’s emerging objective to reconcile two interconnected forms of waste: (1) the material degradation of water attributed to Michigan’s advanced capitalist economy, and (2) the wasted economic potential long used to denigrate Indigenous societies that “failed” to extract capitalist value from nature. This case study demonstrates how Michigan’s discursive approach to managing a potential crisis of capitalist reproduction also reconfigured the logic of improvement as the racial and economic basis for settler colonial authority over nature. “Improving” nature was not only about facilitating access to nature for capitalist production, but reproducing—indefinitely—the ecological conditions on which capitalist production relied. This article builds on two lines of inquiry in critical geographic scholarship exploring mutually constitutive relationships between race and socionatural metabolisms, and between settler colonialism and environmental degradation, to interrogate the postwar discourses flowing through water management in southeast Michigan, a region where water remains at the center of multiple racialized dispossessions and their ongoing contestation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".