Confounding Anti-racism: Mixture, Racial Democracy, and Post-racial Politics in Brazil
Bibliographic record
Abstract
In this article, I analyze the particularity of post-racial ideology in Brazil. I examine recent deployments of mixture and racial democracy as re-articulations of historically hegemonic versions of these ideologies that minimize the problem of racism, deny its systemic nature, and deem ethno-racial policies as threats to achieving nonracial belonging and citizenship. Drawing on scholarship on race and racism from the United States, Brazil, and elsewhere in Latin America, I delineate a relational framework for analyzing the post-racial and apply this framework to three examples of post-racial ideology. Through these examples, I illustrate the problematic logics shaping aggressive investments in the post-racial as future promise to the detriment of addressing the unequal effects racial difference presents for inclusion/exclusion today. The article asserts the necessity of mounting transnational and interdisciplinary theoretical, epistemological, and practical strategies to challenge the ways post-racial ideologies rearticulate racial hierarchies, maintain racial subordination, and delimit social change.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".