AS TICs E A PARTICIPAÇÃO SOCIAL: UMA ANÁLISE DOS GOVERNOS FHC, LULA E DILMA
Bibliographic record
Abstract
O presente artigo discute como se deu a utilização das Tecnologias da Informação e Comunicação (TICs) nos governos de Fernando Henrique Cardoso (FHC) (1995-2002) do PSDB, e Lula (2003-2010) e Dilma (2011-2016) do PT. Parte-se da hipótese de que tais governos responderam de forma diferenciada aos desafios colocados à gestão pública pelas novas configurações tecnológicas. Como recurso metodológico, procedeu-se à analise documental amparada por pesquisa bibliográfica pertinente ao tema. Concluiu-se pela comprovação da hipótese ao se constatar que nos governos FHC o uso das TICs foi guiado pelos princípios da eficiência e da transparência governamental e nos governos Lula e Dilma, prevalecem os usos de interação e reciprocidade com o cidadão
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".