Saberes docentes para atuação na sala de recursos de alunos com deficiência visual
Bibliographic record
Abstract
Este artigo representa uma pesquisa desenvolvida para o curso de Especializacao em Educacao Especial: Area de Deficiencia Visual, desenvolvido pelo Nucleo de Educacao a Distância, da Universidade Estadual Paulista “Julio de Mesquita Filho”. Considerando a importância do professor no processo de inclusao educacional de estudantes publico-alvo da Educacao Especial, o objetivo desta pesquisa foi levantar os saberes docentes de tres professoras que realizam atendimento em salas de recursos de escolas da rede estadual paulista junto a estudantes com deficiencia visual. Devido a sua natureza foi adotada a pesquisa de abordagem qualitativa e se utilizou a tecnica da entrevista semiestruturada para a coleta de dados. Por meio dos relatos, foi possivel verificar que os saberes disciplinares adquiridos na formacao academica influenciam as praticas pedagogicas e os saberes experienciais favoreceram a intervencao e o aprimoramento profissional. No entanto, alguns cursos de formacao inicial e continuada, segundo as participantes, nem sempre oferecem contribuicoes significativas para a atuacao docente nessa area. Diante dessas observacoes, ressaltamos a necessidade de se avaliar a estruturacao desses cursos, levando em consideracao a realidade da pratica docente, para que formacoes sejam oferecidas de maneira que realmente atendam as necessidades profissionais.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".