Multicultural Efforts and Affirmative Action in Brazil: Policies Influencing Education in the Americas
Bibliographic record
Abstract
The purpose of this study was to explore the intercultural movements toward social justice in education in the Americas, most particularly, North America, and how U.S. multicultural movements and policies influence countries like Brazil. First we analyzed the movement toward multicultural practices to understand how those are developed both in the U.S., and in Brazil. We examined multicultural education as a means to generate equal academic access for students from diverse gender, race, culture, and social class. Following, we expanded our understanding of multicultural practices by examining the Affirmative Action as a social justice movement. We asked whether policies can be interculturally adopted, and adapted, to create social justice in educational systems across different countries in the Americas. This study explores the intercultural movements toward social justice in education in the Americas, most particularly, North America, and how U.S. multicultural movements and policies influence countries like Brazil. First we analyzed the movement toward multicultural practices to understand how those are developed both in the U.S., and in Brazil. We examined multicultural education as a means to generate equal academic access for students from diverse gender, race, culture, and social class. Following, we expanded our understanding of multicultural practices by examining the Affirmative Action as a social justice movement. We asked whether policies can be interculturally adopted, and adapted, to create social justice in educational systems across different countries in the Americas.
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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 | 0.005 | 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".