PFAS ubiquity as corporate accomplishment: Whiteness in early Teflon advertisements
Bibliographic record
Abstract
Abstract In recent years, per‐ and polyfluoroalkyl substances (PFAS) contamination has attracted significant media attention. However, little is known about the efforts of chemical corporations to produce consumer markets for PFAS in the Global North. In this article, we reframe PFAS contamination by shifting from characterising PFAS as “emerging contaminants” owing to a prior state of public ignorance, to understanding ubiquitous PFAS exposure as an indicator of environmental violence under colonial racial capitalism. We examine how chemical manufacturers constructed U.S. consumer markets for PFAS‐containing products in the aftermath of World War II. To better understand contemporary PFAS contamination, we turn to the initial military applications of PFAS in the Manhattan Project and examine subsequent corporate efforts to construct civilian markets for synthetic nonstick products in the mid‐twentieth century. Using archival data from advertising campaigns for nonstick cookware, we analyse the roles of white, heterosexual, feminine imagery in early market development. We argue that imagery of white women facilitated the initial normalisation and expansion of domestic chemical markets in the post‐war period. We elevate the work of corporate actors to construct, maintain, and expand markets for PFAS, arguing that these organisations—and the systems that permit their behaviour—are worthy of further study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".