A system dynamics model of science, technology and innovation policy to sustain regional innovation systems in emerging economies
Bibliographic record
Abstract
System dynamics (SD) models have become an important tool to develop new theories in social sciences. This approach allows analysing science, technology and innovation (STI) policy within the structure of the system where this process is carried out. In this regard, the main objective of this research is two-fold. First, it aims to develop an SD model of a RIS in the case of emerging economies. Second, it aims to demonstrate how a set of STI indicators can be simulated with this model. In this paper, it is argued that STI indicators are needed to design a timely and accurate STI policy that support innovation activity at a regional level. However, the SD approach provides an adequate framework to integrate into the same analysis key institutions that support the generation and diffusion of technology and new knowledge. The case of the RIS of the province of Michoacán in Mexico is analysed in this paper.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.033 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".