Investigating Educational Responses to Diversity in Brazil during a Time of Curriculum Change
Bibliographic record
Abstract
This article offers findings from a qualitative case-based research study examining the ways educators in central Brazil made sense of diversity, and the extent to which they believed recent policies in Brazil promoting greater recognition of ethno-cultural diversity are being realized in K-12 contexts. The multinational research team also examined the degree to which these educators felt that responses to diversity drawn from the Canadian context could inform Brazilian educational policy. Of note, the research participants articulated productive possibilities for promoting the inclusion of cultural diversity in varied classroom contexts. However, confirming findings from prior research, they saw recent policy shifts in Brazil related to intercultural understanding as unsupported by institutions, and thus almost completely reliant on teacher’s personal efforts and convictions. Overall, educators in this study had difficulty seeing Canadian responses to diversity as workable in Brazil, and there were a general absence of discussions concerning the teaching of Afro-Brazilian and Indigenous culture and history. Informed by insights from both sociocultural theorizing (Barton & Levstik, 2004; Wertsch, 1998) and transformative learning theory (Freire, 1970; Mezirow, 1991), findings are analyzed by working to uncover the historically derived interpretive frameworks that both enabled and constrained the various beliefs of these educators.
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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 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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".