REFERENCIAL CURRICULAR AMAZONENSE PARA A EDUCAÇÃO INFANTIL:
Bibliographic record
Abstract
A aprovação da Base Nacional Comum Curricular (BNCC) no Brasil desencadeou, conforme previsto no seu Programa de Apoio à Implementação, a revisão ou elaboração de currículos subnacionais pelos estados da federação alinhados à Base. O presente artigo insere-se nesse cenário de emergência e atualização desses currículos no país a partir de 2017, pelo que tomamos à análise o Referencial Curricular Amazonense para a Educação Infantil (RCA-EI). Com o objetivo de compreender os processos de construção do RCA-EI e suas implicações para a educação de crianças no Amazonas, realizamos um estudo exploratório de abordagem qualitativa que se baseou em frentes de revisão de literatura e análise documental. Nossos resultados apontam avanços significativos, sobretudo no que se refere à participação de representantes de diferentes municípios, sindicatos e docentes do ensino superior na elaboração do documento, de um lado; e tensões e contradições ligadas às concepções de infância, aprendizagem e desenvolvimento no interior do texto do RCA-EI, de outro.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".