O eixo Articulador Educação Inclusiva e Especial do Curso de Pedagogia Univesp/ Unesp: análise quanti-qualitativa
Bibliographic record
Abstract
Visando a disseminacao da cultura de uso de tecnologias a servico da educacao, estabelecendo redes de formacao e construcao do conhecimento, para uma escola de qualidade para todos, a Universidade Virtual do Estado de Sao Paulo (Univesp), em parceria com a Universidade Estadual Paulista “Julio de Mesquita Filho” (Unesp) propos, de 2009 a 2013, um curso de Licenciatura em Pedagogia semipresencial, voltado a professores da rede em exercicio, para a formacao nessa licenciatura. O curriculo do curso, estruturado em Blocos de Conteudos, foi composto por Eixos Articuladores, entendidos como disciplinas balizadoras de aspectos transversais aos conteudos da formacao. O Eixo Articulador Educacao Inclusiva e Especial foi desenvolvido no segundo bloco do curso, mediante 120 horas. Neste artigo sera descrita a avaliacao quanti-qualitativa do Eixo Articulador Educacao Inclusiva e Especial, considerando a consolidacao da abordagem Construcionista, Contextualizada e Significativa (CCS), definida por Schlunzen (2005) e presente na tese de Santos (2014). Com base em um questionario aplicado em formato de amostragem, serao apresentadas as perspectivas dos professores-cursistas em relacao as contribuicoes do Eixo no que se refere ao conhecimento sobre politicas e praticas de inclusao escolar e uso de recursos tecnologicos na elaboracao de Planos de Ensino Inclusivos (PEI).
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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 | 0.034 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".