Racial Capitalism and the Propaganda of Conservative Economics
Bibliographic record
Abstract
Racial capitalism has been an active terrain of political economy debate since the 1970s, but the last 5 years have seen a wider diffusion of the concept. We identify one modern component of racial capitalism that has seldom been discussed in extant work: the role of conservative economics at legitimating racial capitalist processes. To this end, we raise the following question: What does a narrative of support for racial capitalism look like in contemporary political economies, where racism denial is pervasive in political discourse, and trust in authorities are at an all-time low? We submit that narratives legitimating contemporary racial capitalism exist, but they are more subtle, indirect, and more plausibly deniable than the narratives that supported chattel slavery and the 100 years of Jim Crow that followed. The Civil Rights Era provided a legal basis for anti-discrimination efforts previously diluted by American jurisprudence and law. In this essay, we engage in a broader conversation about the intersections between discourse and structure before explicating exactly how conservative economics supports and reinforces racial capitalism. Explicating the components of this architecture is crucial to illustrating the value of racial capitalist approaches within the political economy canon.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".