Efeitos de Tamanho da Sala no Desempenho dos Alunos: Evidências Usando Regressões Descontínuas no Brasil
Bibliographic record
Abstract
Este estudo utiliza a metodologia de regressão descontínua fuzzy, considerando o tamanho da sala predito pela função de Maimonides (Angrist & Lavy, 1999) como instrumento para o tamanho da sala observado, para avaliar o impacto de políticas públicas que estipulem um número máximo de alunos por turma no Brasil. Em particular, foram analisados os efeitos de resoluções e portarias municipal e estadual de São Paulo, Minas Gerais e Santa Catarina sobre as notas de alunos do 5º e 9º ano na Prova Brasil 2015. Os resultados mostram que não existam evidências estatisticamente significantes de que o tamanho da sala tenha impacto nas notas dos alunos. As análises de robustez performadas também concluem não haver efeito.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".