Evolução da mobilidade educacional e da acumulação do capital humano no Brasil entre 1996 e 2014: os desafios para subgrupos da população
Bibliographic record
Abstract
Resumo O presente estudo pretende averiguar o crescimento da acumulação do capital humano no Brasil entre os anos de 1996 e 2014 para subgrupos da população. A partir dos dados do suplemento de mobilidade educacional da Pesquisa Nacional de Amostra de Domicílios dos referidos anos e o uso da metodologia do Processo de Markov combinado com aplicação da Decomposição de Blinder-Oaxaca foi possível detectar ganhos diferenciados de acúmulo de capital humano para os grupos analisados. Os resultados apontam que houve uma melhoria considerável da educação para as mulheres e ainda para os indivíduos declarados pretos e os filhos corresidentes, entretanto, chama atenção a baixa mobilidade educacional para as pessoas residentes no setor rural. Esta pesquisa inova ao incorporar as mulheres e os filhos dependentes na amostra, bem como ao analisar os diferenciais de acumulação de capital humano entre subgrupos populacionais.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".