Distributing Reproduction Under Racial Capitalism: Crises and Conjunctures of Human Milk Banking in South Africa
Bibliographic record
Abstract
In 2011 milk banks became key components of the public health establishment in South Africa, when the country committed to promoting exclusive breastfeeding to fight infant morbidity and mortality. In this article I present a conjunctural analysis of how donor human milk banks are social reproductive infrastructures that facilitate the flow of milk at a space-time of racial capitalist infectious disease and neoliberal care crises. I ask: How do multiscalar governing logics, discourses, and technologies articulate to make this donor economy a conjunctural form of distributed social and biological reproduction? How have various interlocking economic, ecological, and reproductive crises underpinned the emergence of milk banking infrastructures? And, methodologically, how can conjunctural analysis fold in necessary attention to the embodied, biological, biomedical, and religious factors that shape social reproductive politics in a given space-time? I argue that this social reproductive infrastructure is molded through the articulation of numerous discourses and processual logics that dialectically entwine the global and local, including socioecological and embodied crisis, biomedicalization, networks of kin and care, and secularization. In so doing I build out a conjunctural analytic attentive to social reproduction under racial capitalism that privileges the body, ecologies, kinship, and religious-cultural norms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".