The significance of post-racial ideology, black political struggle, and racial literacy for Brazilian anti-racist education policy
Bibliographic record
Abstract
This paper furthers current analysis of anti-racist, critical multicultural, and decolonial educational reforms in Brazil through a focus on the significant role played by post-racial ideology, black politics, and racial literacy in policy design and implementation. The paper first details the ways in which post-racial commonsense and anti-black racism have been central to the Brazilian social formation and continue to constitute crucial obstacles to fundamentally reshaping the curriculum, educational institutions, educators’ racial literacy, and classroom pedagogies. The article then contends that understanding the politics of race and education in Brazil necessitates acknowledging emergent anti-racist policies and discourses as the product of decades of black political struggle by activists, educators, and community organizations to make racism and racial inequality public issues. In this way, the policy documents and discourses shaping recent educational reforms in Brazil should be understood as political interventions within a particular historical conjuncture and racial formation. Such an analysis reveals contemporary black Brazilian efforts in education as mobilizations that go beyond a ‘politics of identity,’ recognition and apolitical multiculturalism and towards building more transformative anti-racist and decolonial proposals that directly challenge the nature and effects of anti-black racism in society.
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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 | 0.006 | 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 | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".