Training educators in anti-racism and<i>pluriculturalismo</i>: recent experiences from Brazil
Bibliographic record
Abstract
This article examines educator participation in training initiatives based on Brazilian federal education legislation (Law 10,639 from 2003) in one city in the state of São Paulo. Law 10,639/03 represents a significant moment in the institutionalization of ethno-racial policies in Brazil over the past 15 years. It makes obligatory the teaching of African and Black Brazilian history and culture in all school subjects, and requires in-depth study of black contributions in the social, economic, and political spheres. The article first contextualizes understandings of race and racism in Brazil, followed by an elaboration of the political and epistemological underpinnings of ethno-racial educational reforms focused on Afro-descendants. The article then analyzes the contradictory processes that emerge from teacher training initiatives where the perspectives of anti-racism, multiculturalism (pluriculturalismo), racial democracy, and miscegenation intermingle and get reconfigured into understandings that have the potential to advance as well as impede critical engagement with racism and racial inequality. Rather than view teacher training initiatives as default decolonization or inevitable co-optation, this article outlines a more complex and contradictory account of state-society collaborations on educational initiatives. The article reveals the practical challenges of decolonization to argue that anti-racist activism in the educational sphere must take seriously the variable and contingent results of such political efforts in order to meet teachers where they are at while also challenging them to go beyond these limitations.
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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 | 0.009 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".