Community leadership and the Triple Helix model as determinants of the constitution of science parks
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze and understand the condition that lead to a constitution’s path of Brazilian Science Parks, in the State of Rio Grande do Sul, and consequently to propose a new dimension of analysis to the Triple Helix. Design/methodology/approach A qualitative approach was used to elaborate a descriptive and exploratory research design, where a case study method was applied on six science parks. Findings The use of the Triple Helix model could not explain the Brazilian Science Park development realities. A new element, related to the innovation model, was considered as a determinant in the constitution of the Brazilian parks, and is represented as the community leadership category, as the Fourth Helix. Research limitations/implications Since it is a qualitative study, the results obtained have a strong relation with the local, cultural and historically constructed contexts. Bias from the researchers’ subjectivity in the data collection procedures is present, although the validity and reliability measures were performed. Practical implications The construction of designed and implemented specific “fertile models,” which are capable of developing the necessary conditions for the constitution and the consolidation of science parks in Brazil. Social implications Such empirical contribution comes from data referring to spontaneous and endogenous local community development movements. Originality/value The identification of a new element of the Triple Helix innovation model is represented as the community leadership category and is considered as a key determinant in the constitution of the Brazilian Science Parks.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".