AMBIENTES DE INOVAÇÃO: DISCUTINDO O ECOSSISTEMA DO QUARTIER DE L’INNOVATION
Bibliographic record
Abstract
Este trabalho tem como objetivo analisar as áreas de inovação e sua importância no desenvolvimento local e regional. Para tanto, o objeto principal de estudo será o Ecossistema de Inovação denominado Quartier de L’Innovation, localizado no Québec, no Canadá. Esse ambiente se constitui em um dos principais modelos de cooperação entre os atores do desenvolvimento regional, caracterizando-se como um dos melhores exemplos de práticas da tríplice hélice no Canadá. Esse estudo abre também a possibilidade da sua análise referendar boas práticas de inovação no Brasil e, especialmente, no Estado Rio Grande do Sul. Palavras-chaves: Inovação. Cooperação. Desenvolvimento regional. Tríplice hélice. ABSTRACT This work aims to analyze the innovation areas and its importance in local and regional development. Therefore, the main object of study is the Innovation Ecosystem called Quartier de L’Innovation, located in Quebec, Canada. This environment constitutes one of the main models of cooperation between the actors of regional development, characterized as one of the best examples of a triple helix practice in Canada. This study also opens up the possibility of its analysis to endorse good innovation practices in Brazil, especially in Rio Grande do Sul State. Keywords: Innovation. Cooperation. Regional development. Triple helix.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".