A efetividade dos instrumentos de financiamento público da FINEP no desenvolvimento do APL eletroeletrônico de Santa Rita do Sapucaí
Bibliographic record
Abstract
The main objective of the study was to understand effectiveness instruments of public financiai subsidies at APL Eletroeletrônico of Santa Rita do Sapucaí, MG by Financier of Studies and Projects (Financiadora de Estudos e Projetos - FINEP) focusing the engine company of APL, Linear - Equipamentos Eletrônicos S/A. In This study, the principal models on regional development: Local Productive Arrangements (APLs) and Growth Poles were emphasized, because both of them characterized the studied region. The Technological Innovation Law (Lei da Inovação Tecnológica - LlT), which is the main supporter of APLs in Brazil, also was described, as well as financiai agents, the current mechanisms and financing instruments of FINEP and how they are available. Based on the established public policies, by the Federal Government and by the Ministry of Science and Technology (Ministério da Ciência e Tecnologia - MCT) to which FINEP is linked. The study analysed, the context of the APL Eletroeletrônico of Santa Rita do Sapucaí in the case of Linear company through the public financing instruments established for the development of above-mentioned APL. As conclusion to the raised initial question on research it can be understood if there was effectiveness with the public instruments of financing of FINEP on development of APL Eletroeletrônico of Santa Rita do Sapucaí.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".