análise sobre os fatores que influenciaram no resultado financeiro dos investimentos realizados pelo FMIEE CRIATEC
Bibliographic record
Abstract
O objetivo deste artigo é analisar os investimentos realizados pelo Fundo Criatec, buscando identificar quais fatores influenciaram o seu resultado financeiro. Para isso, foram determinadas métricas de retorno financeiro dos investimentos e variáveis para a análise dos fatores qualitativos e quantitativos que influenciaram o resultado dos investimentos do Criatec. Cada um dos fatores foi relacionado com o resultado financeiro, através da análise de cluster hierárquico sendo determinados grupos de empresas com caraterísticas similares e com mais chance de retornos positivos. Os resultados indicaram que ter sócios com qualificação ou experiência em gestão se mostrou um fator expressivo no desempenho das empresas apoiadas pelo Criatec. Outro fator diferencial foi o grau de inovação,sendo que as empresas com tecnologias mais disruptivas apresentaram um resultado financeiro inferior às empresas com produtos sem grandes diferenciais tecnológicos.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".