Análise preliminar do impacto da BNCC no Ensino Médio, no contexto do ensino de Geografia
Bibliographic record
Abstract
O objetivo desta pesquisa foi analisar os impactos da reforma curricular da educacao basica brasileira, normatizada pela Base Nacional Comum Curricular (BNCC), sobretudo no tocante ao ensino da Geografia no Ensino Medio. Para isso, foi utilizada uma metodologia documental tendo como base as leis as leis de Diretrizes e Bases da Educacao Nacional (BRASIL, 1996), o Plano Nacional da Educacao (BRASIL, 2014), a Reforma do Ensino Medio em tempo integral (BRASIL, 2017) e a BNCC (BRASIL, 2018), aliado as reflexoes cientificas acerca do ensino de Geografia, ancorado em Girotto (2016), Guimaraes (2018), Corti (2019), Freire (2019) e Laval (2019). Destarte, foi constatado que a homologacao da BNCC (BRASIL, 2018) ocorreu em meio a uma disputa de interesses a respeito do papel da educacao na sociedade. Nessa perspectiva, constatou-se que o papel do componente curricular Geografia sofreu impacto direto ficando aquem do seu potencial pedagogico, pois sem assegurar uma discussao pedagogica com os(as) docentes e estudantes de Geografia em todo territorio brasileiro, o Ministerio da Educacao (MEC) impos a reducao da carga horaria do componente curricular Geografia no curso de Ensino Medio, restringindo e diluindo o volume de conteudo a uma perspectiva interdisciplinar entre os componentes curriculares que compoe as Ciencias Humanas, isto e, Geografia, Historia, Filosofia e Sociologia. Desse modo, nesta reflexao teorica, sao indicados os tensionamentos politicos e pedagogicos existentes no processo de ensino e aprendizagem do componente curricular Geografia, a luz da reforma curricular estabelecida pela BNCC (BRASIL, 2018).
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".