Bibliographic record
Abstract
Introdução-- Um sistema de promoção de efetividade para o BNDES -- Os avanços recentes -- A Tese de Impacto de Investimento em Projetos (Tiip) -- Quadro de Resultados (QR) -- A economia brasileira no biênio 2015-2016 -- Esforço do BNDES: desempenho na perspectiva interna -- Participação no PIB e na formação bruta de capital fixo -- Desembolsos por prioridades corporativas -- Número de empresas apoiadas -- processo de monitoramento: os resultados da atuação do BNDES -- Infraestrutura (energia, logística e mobilidade urbana) e gestão pública -- Indústria, comércio e serviços -- Inovação -- Inclusão social e produtiva e sustentabilidade -- Mercado de capitais -- Geração ou manutenção de empregos -- Os impactos do BNDES -- O que são avaliações de impacto? -- Revisando as avaliações de impacto sobre o BNDES -- Avaliações automatizadas de impacto: o uso do Marvim -- Avaliações customizadas -- Balanço e perspectivas -- Detalhamento de avaliações do Marvim: casos selecionados do módulo de pareamento; Equipe técnica: Arthur Rezende (AP/DEAPE), Breno Albuquerque (AP/DEAPE), Daniel Grimaldi (AP/DEAPE), Débora Duque Estrada (AP/DEAPE), Fábio Roitman (AP/DEAPE), Guilherme Pereira (AP/DEAPE), Leonardo Santos (AP/DEAPI), Marcus Tortorelli (AP/DEAPE), Paulo Azzi (AP/DEAPE), Paulo Faveret (ACRI/DERI), Ricardo Martini (AP/DEAPE), Victor Pina (AP/DEAPE).
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".