The Science and Technology Parks (STPs) Evaluation Model Approach to Eco-Innovation Key Indicator
Bibliographic record
Abstract
Science and Technology Park (STP) is one of the most important innovation policies to develop the regional economy. To manage the STPs successfully, a standard evaluation system as a reference is needed. However, there is no consensus about the definition of successful STPs due to their different goals and regions. Hence, it is necessary to establish a reference framework to evaluate the success of different STPs and it is essential to assess their main goals as the competitive advantage by a set of innovation indicators. This study developed a research model to evaluate the competitiveness of STPs by analyzing the impact of innovation subjective externalities based on the Global Innovation Index (GII) and approach to the eco-innovation key indicator. This STP evaluation model is adopted and tested by two different fuzzy analyzing and examines the survey forms and questionnaires that have been filled by some STP experts and as a case study all the evidence has been gathered and analyzed from “Caohejing Hi-Tech Park” in Shanghai and the results evaluated the competitive advantage via innovation policies and performances and the rating rank contents some innovation main dimensions, key indicators and factors and also the important result as eco-innovation development and diffusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".