An economy of immunity: The racial‐spatial lives of antibodies in the American blood plasma economy from 1960s prisons to COVID‐19
Bibliographic record
Abstract
Abstract In 2020, a market in convalescent blood plasma developed as a potential treatment for COVID‐19. During this time, commercial plasma centres—which collect the blood plasma from paid donors for pharmaceutical production—paid recovered patients as much as US$100 for a donation of blood plasma containing COVID‐19 antibodies, from which they manufactured an experimental treatment. This paper uses the commercial collection of COVID‐19 antibodies found in plasma as an entry point into exploring how racially uneven exposures to disease may produce biovalue. The first section considers the spatial history of antibody‐derived plasma products, using historical research to examine how inmates at predominantly black plantation‐prisons in the US South were valued for antibody production in the 1960s. Against this historical relief, the second section examines the spatiality of antibodies in current practices of the plasma industry, as well as in the plasma industry's response to the COVID‐19 pandemic (even as those efforts eventually failed in clinical trials). If geographical literatures on environmental exposure, as well as on COVID‐19 transmission, discuss exposure as an outcome of racial capitalism, this paper emphasises the productive opportunities that capital can solicit from exposures, especially those that are useful to different forms of biomedicine. By critically scrutinising the practice of ‘sharing immunity’ and attending to its decidedly geographic constitution, we can see how what Ruth Wilson Gilmore calls the ‘death‐dealing logics’ of racial capitalism may also work through the seemingly affirmative practices and communal imaginaries behind the redistribution of antibodies as medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".